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This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

Underwater surveillance using passive sonar and track-before-detect technology requires accurate models of the tracked signal and the background noise. However, in an underwater environment, the signal channel is time-varying and prior knowledge about the spatial distribution of the background noise is unavailable. In this paper, an autoregressive model that captures a time-varying signal level caused by multi-path propagation is presented. In addition, a multi-source model is proposed to describe spatially distributed background noise. The models are used in a Bernoulli filter track-before-detect framework and evaluated using both simulated and sea trial data. The simulations demonstrate clear improvements in terms of target loss and improved ability to discern the target from the noisy background. An evaluation of the track-before-detect algorithm on the sea trial data indicates a performance gain when incorporating the proposed models in underwater surveillance and tracking problems.

This paper proposes the modeling and analysis of multi-energy systems as multilayer networks. The aim is to assess the interdependence between different energy infrastructures. Multilayer network modeling enhances the one-dimensional graph-based approach employed to study the vulnerability and the topological characteristics of power grids. The centrality indices defined for one-dimensional networks are extended to multilayer networks, highlighting the differences emerging when interdependence is considered. The proposed multilayer framework is demonstrated on the 24-bus IEEE power grid coupled with a gas network with 7 nodes.

Hegselmann-Krause (HK) models exhibit complex behaviors which are not easily tractable through mathematical analysis. In this paper, a characterization of the steady-state behaviors of homogeneous HK models and sensitivity to confidence thresholds is discussed by commenting on existing and new numerical results. The typical decreasing of number of clusters and convergence time by increasing the confidence thresholds are discussed and motivations for the behavior of some counterexamples are provided. A tighter upper bound for the dependence of the number of clusters with respect to the confidence thresholds is proposed. Differences and analogies between the opinions evolution for symmetric and asymmetric HK models are commented.

Recent studies of selective auditory attention have demonstrated that neural responses recorded with electroencephalogram (EEG) can be decoded to classify the attended talker in everyday multitalker cocktail-party environments. This is generally referred to as the auditory attention decoding (AAD) and could lead to a breakthrough for the next-generation of hearing aids (HAs) to have the ability to be cognitively controlled. The aim of this paper is to investigate whether cepstral analysis can be used as a more robust mapping between speech and EEG. Our preliminary analysis revealed an average AAD accuracy of 96%. Moreover, we observed a significant increase in auditory attention classification accuracies with our approach over the use of traditional AAD methods (7% absolute increase). Overall, our exploratory study could open a new avenue for developing new AAD methods to further advance hearing technology. We recognize that additional research is needed to elucidate the full potential of cepstral analysis for AAD.

An autonomous underwater vehicle (AUV) is a crewless robotic vehicle that dives into the water and performs without human assistance. This paper focuses on developing trajectory tracking control for bio-mimetic AUV system under uncertain environments. Therefore, a relatively new control technique called time delay-based estimation control is proposed for trajectory tracking under multiple uncertainties. This algorithm estimates the total disturbance in the system using immediate past information of input and output of feedback state and control variables. The benefit of this scheme is that it avoids assumptions about a priori upper bound information of disturbance. Further, the control structure is simple and does not require any high-frequency switching or high gain to nullify the effects of disturbance. The theoretical analysis of the proposed scheme guarantees the uniformly ultimate bounded stability of the closed-loop system. The numerical analysis is also carried out to validate the control performance of the given algorithm for lemniscate reference path tracking.

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown., Funding Agencies|Swedens Innovation Agency

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown., Funding Agencies|Swedens Innovation Agency

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

Asymptotic theory for the regularized system identification has received increasing interests in recent years. In this paper, for the finite impulse response (FIR) model and filtered white noise inputs, we show the convergence in distribution of the Steins unbiased risk estimator (SURE) based hyper-parameter estimator and find factors that influence its convergence properties. In particular, we consider the ridge regression case to obtain closed-form expressions of the limit of the regression matrix and the variance of the limiting distribution of the SURE based hyper-parameter estimator, and then demonstrate their relation numerically., Funding Agencies|Thousand Youth Talents Plan funded by the central government of China - NSFC [61773329]; Shenzhen Science and Technology Innovation Council [Ji-20170189, JCY20170411102101881]; Robotic Discipline Development Fund from Shenzhen Government [2016-1418]; CUHKSZ [PF. 01.000249, 2014.0003.23]; Swedish Research Council [2019-04956]; Vinnovas center LINKSIC

If a concatenated Friedkin-Johnsen model is used to describe the evolution of the opinions of stubborn agents in a sequence of discussion events, then the social power achieved by the agents at the end of the discussions depends from the stubbornness coefficients adopted by the agents through the sequence of events. In this paper we assume that the agents are free to choose their stubbornness profiles, and ask ourselves what strategy should an agent follow in order to maximize its social power. Formulating the problem as a strategic game, we show that choosing the highest possible values of stubbornness in the early discussions leads to the highest possible social power., Funding Agencies|Swedish Research Council [2020-03701]; China Scholarship Council CSC [202004910739]; Shanghai Municipal Science and Technology Major Project [2021SHZDZX0100]; National Natural Science Foundation of China [61733018]

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown., Funding Agencies|Swedens Innovation Agency

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Laplacian dynamics on signed graphs have a richer behavior than those on nonnegative graphs. In particular, their stability is not guaranteed a priori. Consequently, also the time-varying case must be treated with care. In particular, instabilities can occur also when switching in a family of systems each of which corresponds to a stable signed Laplacian. In the paper we obtain sufficient conditions for such a family of signed Laplacians to form a consensus set, i.e., to be stable and converging to consensus for any possible switching pattern. The conditions are that all signed Laplacian matrices are eventually exponentially positive (a Perron-Frobenius type of property) and normal., Funding Agencies|Swedish Research Council [2020-03701]; Shanghai Municipal Science and Technology Major Project [2021SHZDZX0100]; National Natural Science Foundation of China [61733018]

Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown., Funding Agencies|Swedens Innovation Agency

Raman spectroscopy in conjunction with a Coded Aperture Snapshot Spectral Imaging (CASSI) system allows for detection of small amounts of explosives from stand-off distances. The obtained Compressed Sensing (CS) measurements from CASSI consists of mixed spatial and spectral information, from which a HyperSpectral Image (HSI) can be reconstructed. The HSI contains Raman spectra for all spatial locations in the scene, revealing the existence of substances. In this paper we present the possibility of utilizing a learned prior in the form of a conditional generative model for HSI reconstruction using CS. A Generative Adversarial Network (GAN) is trained using simulated samples of HSI, and conditioning on their respective CASSI measurements to refine the prior. Two different types of simulated HSI were investigated, where spatial overlap of substances was either allowed or disallowed. The results show that the developed method produces precise reconstructions of HSI from their CASSI measurements in a matter of seconds., Funding Agencies|Security Link project "Compressive Sensing for spektral detektion av explosivamnen"

Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown., Funding Agencies|Swedens Innovation Agency

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Asymptotic theory is one of the core subjects in system identification theory and often used to assess properties of model estimators. In this paper, we focus on the asymptotic theory for the kernel-based regularized system identification and study the convergence in distribution of the generalized cross validation (GCV) based hyper-parameter estimator. It is shown that the difference between the GCV based hyper-parameter estimator and the optimal hyper-parameter estimator that minimizes the mean square error scaled by 1/root N converges in distribution to a zero mean Gaussian distribution, where N is the sample size and an expression of covariance matrix is obtained. In particular, for the ridge regression case, a closed-form expression of the variance is obtained and shows the influence of the limit of the regression matrix on the asymptotic distribution. For illustration, Monte Carlo numerical simulations are run to test our theoretical results., Funding Agencies|Thousand Youth Talents Plan funded by the central government of China - NSFC [61773329]; Shenzhen Science and Technology Innovation Council [Ji-20170189, JCY20170411102101881]; Robotic Discipline Development Fund [20161418]; Shenzhen Government [2014.0003.23]; CUHKSZ; Swedish Research Council [2019-04956]

A concatenated Friedkin-Johnsen (FJ) model is a two time-scale opinion dynamics model in which stubborn agents discuss a sequence of issues. For each issue, a FJ model is adopted, and concatenation refers to the fact that the final opinion of the agents at issue s becomes the initial condition at issue s + 1. In this paper we deal with the case in which the system is open, i.e., the group of interacting agents changes at each issue, and so does their stubbornness. A concatenated FJ model can in this case be represented as an infinite product of stochastic matrices. For such system, we obtain sufficient conditions under which the opinions of the agents converge to consensus., Funding Agencies|Swedish Research Council [2020-03701]

The pseudoinverse of a graph Laplacian is used in many applications and fields, such as for instance in the computation of the effective resistance in electrical networks, in the calculation of the hitting/commuting times for a Markov chain and in continuous-time distributed averaging problems. In this paper we show that the Laplacian pseudoinverse is in general not a Laplacian matrix but rather a signed Laplacian with the property of being an eventually exponentially positive matrix, i.e., of obeying a strong Perron-Frobenius property. We show further that the set of signed Laplacians with this structure (i.e., eventual exponential positivity) is closed with respect to matrix pseudoinversion. This is true even for signed digraphs, and provided that we restrict to Laplacians that are weight balanced also stability is guaranteed., Funding Agencies|Swedish Research Council [2020-03701]; Swedish ELLIIT program

When solving a quadratic program (QP), one can improve the numerical stability of any QP solver by performing proximal-point outer iterations, resulting in solving a sequence of better conditioned QPs. In this paper we present a method which, for a given multi-parametric quadratic program (mpQP) and any polyhedral set of parameters, determines which sequences of QPs will have to be solved when using outer proximal-point iterations. By knowing this sequence, bounds on the worst-case complexity of the method can be obtained, which is of importance in, for example, real-time model predictive control (MPC) applications. Moreover, we combine the proposed method with previous work on complexity certification for active-set methods to obtain a more detailed certification of the proximal-point methods complexity, namely the total number of inner iterations., Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]

Network reconstruction has become particularly important in systems biology, and is now expected to deliver information on causality. Systems in nature are inherently nonlinear. However, for nonlinear dynamical systems with hidden states, how to give a useful definition of dynamic networks is still an open question. This paper presents a useful definition of Boolean dynamic networks for a large class of nonlinear systems. Moreover, a robust inference method is provided. The well-known Millar-10 model in systems biology is used as a numerical example, which provides the ground truth of causal networks for key mRNAs involved in eukaryotic circadian clocks. In addition, as second contribution of this paper, we suggest definitions of linear network identifiability, which helps to unify the available work on network identifiability., Funding Agencies|Fonds National de la Recherche Luxembourg [AFR-9247977, AFR-8864515]

One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper., Funding Agencies|project Scalable KaIman Filters - Swedish Research Council (VR)

One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper., Funding Agencies|project Scalable KaIman Filters - Swedish Research Council (VR)

One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper., Funding Agencies|project Scalable KaIman Filters - Swedish Research Council (VR)

One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper., Funding Agencies|project Scalable KaIman Filters - Swedish Research Council (VR)

One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper., Funding Agencies|project Scalable KaIman Filters - Swedish Research Council (VR)

In this paper, we study the influence of ill-conditioned regression matrix on two hyper-parameter estimation methods for the kernel-based regularization method: the empirical Bayes (EB) and the Steins unbiased risk estimator (SURE). First, we consider the convergence rate of the cost functions of EB and SURE, and we find that they have the same convergence rate but the influence of the ill-conditioned regression matrix on the scale factor are different: for upper bounds, the scale factor for SURE contains one more factor cond(Phi(T)Phi) than that of EB, where Phi is the regression matrix and cond(.) denotes the condition number of a matrix. This finding indicates that when Phi is ill-conditioned, i.e., cond(Phi(T)Phi) is large, the cost function of SURE converges slower than that of EB. Then we consider the convergence rate of the optimal hyper-parameters of EB and SURE, and we find that they are both asymptotically normally distributed and have the same convergence rate, but the influence of the ill-conditioned regression matrix on the scale factor are different. In particular, for the ridge regression case, we show that the optimal hyper-parameter of SURE converges slower than that of EB with a factor of 1/n(2), as cond(Phi(T)Phi) goes to infinity, where n is the FIR model order., Funding Agencies|Thousand Youth Talents Plan funded by the central government of China - NSFCNational Natural Science Foundation of China (NSFC) [61773329]; Shenzhen Science and Technology Innovation Council [Ji-20170189 (J-CY20170411102101881)]; Robotic Discipline Development Fund from Shenzhen Government [20161418]; CUHKSZ [2014.0003.23]; [PF. 01.000249]

Model Predictive Control (MPC) requires an optimization problem to be solved at each time step. For real-time MPC, it is important to solve these problems efficiently and to have good upper bounds on how long time the solver needs to solve them. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving QPs is primal active-set methods, where a sequence of equality constrained QP subproblems are solved. This paper presents a method for computing which sequence of subproblems a primal active-set method will solve, for every parameter of interest in the parameter space. Knowledge about exactly which sequence of subproblems that will be solved can be used to compute a worst-case bound on how many iterations, and ultimately the maximum time, the active-set solver needs to converge to the solution. Furthermore, this information can be used to tailor the solver for the specific control task. The usefulness of the proposed method is illustrated on a set of MPC problems, where the exact worst-case number of iterations a primal active-set method requires to reach optimality is computed., Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]

Model Predictive Control (MPC) requires an optimization problem to be solved at each time step. For real-time MPC, it is important to solve these problems efficiently and to have good upper bounds on how long time the solver needs to solve them. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving QPs is primal active-set methods, where a sequence of equality constrained QP subproblems are solved. This paper presents a method for computing which sequence of subproblems a primal active-set method will solve, for every parameter of interest in the parameter space. Knowledge about exactly which sequence of subproblems that will be solved can be used to compute a worst-case bound on how many iterations, and ultimately the maximum time, the active-set solver needs to converge to the solution. Furthermore, this information can be used to tailor the solver for the specific control task. The usefulness of the proposed method is illustrated on a set of MPC problems, where the exact worst-case number of iterations a primal active-set method requires to reach optimality is computed., Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]

Model Predictive Control (MPC) requires an optimization problem to be solved at each time step. For real-time MPC, it is important to solve these problems efficiently and to have good upper bounds on how long time the solver needs to solve them. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving QPs is primal active-set methods, where a sequence of equality constrained QP subproblems are solved. This paper presents a method for computing which sequence of subproblems a primal active-set method will solve, for every parameter of interest in the parameter space. Knowledge about exactly which sequence of subproblems that will be solved can be used to compute a worst-case bound on how many iterations, and ultimately the maximum time, the active-set solver needs to converge to the solution. Furthermore, this information can be used to tailor the solver for the specific control task. The usefulness of the proposed method is illustrated on a set of MPC problems, where the exact worst-case number of iterations a primal active-set method requires to reach optimality is computed., Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]

Model Predictive Control (MPC) requires an optimization problem to be solved at each time step. For real-time MPC, it is important to solve these problems efficiently and to have good upper bounds on how long time the solver needs to solve them. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving QPs is primal active-set methods, where a sequence of equality constrained QP subproblems are solved. This paper presents a method for computing which sequence of subproblems a primal active-set method will solve, for every parameter of interest in the parameter space. Knowledge about exactly which sequence of subproblems that will be solved can be used to compute a worst-case bound on how many iterations, and ultimately the maximum time, the active-set solver needs to converge to the solution. Furthermore, this information can be used to tailor the solver for the specific control task. The usefulness of the proposed method is illustrated on a set of MPC problems, where the exact worst-case number of iterations a primal active-set method requires to reach optimality is computed., Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]

Model Predictive Control (MPC) requires an optimization problem to be solved at each time step. For real-time MPC, it is important to solve these problems efficiently and to have good upper bounds on how long time the solver needs to solve them. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving QPs is primal active-set methods, where a sequence of equality constrained QP subproblems are solved. This paper presents a method for computing which sequence of subproblems a primal active-set method will solve, for every parameter of interest in the parameter space. Knowledge about exactly which sequence of subproblems that will be solved can be used to compute a worst-case bound on how many iterations, and ultimately the maximum time, the active-set solver needs to converge to the solution. Furthermore, this information can be used to tailor the solver for the specific control task. The usefulness of the proposed method is illustrated on a set of MPC problems, where the exact worst-case number of iterations a primal active-set method requires to reach optimality is computed., Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]

In this paper, we analyze a Linear Quadratic (LQ) control problem in terms of the average cost and the structure of the value function. We develop a completely model-free reinforcement learning algorithm to solve the LQ problem. Our algorithm is an off-policy routine where each policy is greedy with respect to all previous value functions. We prove that the algorithm produces stable policies given that the estimation errors remain small. Empirically, our algorithm outperforms the classical Q and off-policy learning routines., Funding Agencies|Vinnova Competence Center LINK-SIC; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP)

In this paper, we present a new result on robust adaptive dynamic programming for the Linear Quadratic Regulation (LQR) problem, where the linear system is subject to unmatched uncertainty. We assume that the states of the linear system are fully measurable and the matched uncertainty models unmeasurable states with an unspecified dimension. We use the small-gain theorem to give a sufficient condition such that the generated policies in each iteration of on-policy and off-policy routines guarantee robust stability of the overall uncertain system. The sufficient condition can be used to design the weighting matrices in the LQR problem. We use a simulation example to demonstrate the result., Funding Agencies|Vinnova Competence Center LINK-SIC; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP)

In this paper we show that chordal structure can be used to devise efficient optimization methods for robust model predictive control problems. To this end, first the problem is converted to an equivalent robust quadratic programming formulation. We then illustrate how the chordal structure can be used to distribute the computations in a primal-dual interior-point method among computational agents, which in turn allows us to accelerate the algorithm by efficient parallel computations. We investigate performance of the framework in Julia using numerical examples., Funding Agencies|ELLIIT; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

It has been shown lately that any "standard" Bayesian lower bound (BLB) on the mean squared error (MSE) of the Weiss-Weinstein family (WWF) admits a "tighter" form which upper bounds the "standard" form. Applied to the Bayesian Cramer-Rao bound (BCRB), this result suggests to redefine the concept of efficient estimator relatively to the tighter form of the BCRB, an update supported by a noteworthy example. This paper lays the foundation to revisit some Bayesian estimation problems where the BCRB is not tight in the asymptotic region., Funding Agencies|DGA/MRIS [2018.60.0072.00.470.75.01]; Excellence Center at Linkoping and Lund in Information Technology (ELLIIT)

In this paper we show that chordal structure can be used to devise efficient optimization methods for robust model predictive control problems. To this end, first the problem is converted to an equivalent robust quadratic programming formulation. We then illustrate how the chordal structure can be used to distribute the computations in a primal-dual interior-point method among computational agents, which in turn allows us to accelerate the algorithm by efficient parallel computations. We investigate performance of the framework in Julia using numerical examples., Funding Agencies|ELLIIT; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

In this paper we show that chordal structure can be used to devise efficient optimization methods for robust model predictive control problems. To this end, first the problem is converted to an equivalent robust quadratic programming formulation. We then illustrate how the chordal structure can be used to distribute the computations in a primal-dual interior-point method among computational agents, which in turn allows us to accelerate the algorithm by efficient parallel computations. We investigate performance of the framework in Julia using numerical examples., Funding Agencies|ELLIIT; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

In this paper we show that chordal structure can be used to devise efficient optimization methods for robust model predictive control problems. To this end, first the problem is converted to an equivalent robust quadratic programming formulation. We then illustrate how the chordal structure can be used to distribute the computations in a primal-dual interior-point method among computational agents, which in turn allows us to accelerate the algorithm by efficient parallel computations. We investigate performance of the framework in Julia using numerical examples., Funding Agencies|ELLIIT; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

In this paper we show that chordal structure can be used to devise efficient optimization methods for robust model predictive control problems. To this end, first the problem is converted to an equivalent robust quadratic programming formulation. We then illustrate how the chordal structure can be used to distribute the computations in a primal-dual interior-point method among computational agents, which in turn allows us to accelerate the algorithm by efficient parallel computations. We investigate performance of the framework in Julia using numerical examples., Funding Agencies|ELLIIT; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

By arranging a large number of inertial sensors in an array and fusing their measurements, it is possible to create inertial sensor assemblies with a high performance-to-price ratio. Recently, a maximum likelihood estimator for fusing inertial array measurements collected at a given sampling instance was developed. In this paper, the maximum likelihood estimator is extended by introducing a motion model and deriving a maximum a posteriori estimator that jointly estimates the array dynamics at multiple sampling instances. Simulation examples are used to demonstrate that the proposed sensor fusion method have the potential to yield significant improvements in estimation accuracy. Further, by including the motion model, we resolve the sign ambiguity of gyro-free implementations, and thereby open up for implementations based on accelerometer-only arrays., Funding Agencies|Swedish Foundation for Strategic Research (SSF) via the project ASSEMBLE

Today, thanks to the development of micro-scale low-cost accelerometers, gyroscopes, and magnetometers, it is possible to construct compact motion-sensor arrays with hundreds of sensing elements. These arrays, as well as providing a high performance-to-price ratio, can also provide new measurement capabilities and enable the development of smart sensors that adapt to the usage conditions. This paper gives an overview of the capabilities enabled by the stochastic, component, temporal, and spatial information diversity that an array of inertial or magnetic field sensors provide. The main challenges in the practical realization of these types of arrays are described.

Wireless power transfer technology provides a possible sustainable and cost-effective way to prolong indefinitely the lifetime of networks of smart devices needed in future Internet-of-Things, while equipping them with batteries of limited capacity. In this paper we show that the theory of compartmental systems, positive interconnected systems exchanging mass (here power) and ruled by mass conservation laws, provides a suitable framework to describe wireless power transfer networks. In particular we show that sustainability of the network of smart devices corresponds to each of them being alimented, directly or indirectly, by nodes having an external source of power, condition known as inflow connectivity in the compartmental systems literature. The framework allows to compute the topology which is optimal in terms of maximizing the overall efficiency of the power transfer., Funding Agencies|Swedish Research Council [2015-04390]

This paper studies the asymptotic properties of the hyperparameter estimators including the leave-k-out cross validation (LKOCV) and r-fold cross validation (RFCV), and discloses their relation with the Steins unbiased risk estimators (SURE) as well as the mean squared error (MSE). It is shown that as the number of data goes to infinity, the LKOCV shares the same asymptotic best hyperparameter minimizing the MSE estimator as the SURE does if the input is bounded and the ratio between the training data and the whole data tends to zero. We illustrate the efficacy of the theoretical result by Monte Carlo simulations., Funding Agencies|National Natural Science Foundation of China [61603379, 61773329]; National Key Basic Research Program of China (973 Program) [2014CB845301]; President Fund of Academy of Mathematics and Systems Science, CAS [2015-hwyxqnrc-mbq]; Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Chinese University of Hong Kong, Shenzhen [2014.0003.23]; Swedish Research Council [2014-5894]; Thousand Youth Talents Plan - central government of China; [PF. 01.000249]

In this work we consider a nonlinear interconnected system describing a decision-making process in a community of agents characterized by the coexistence of collaborative and antagonistic interactions. The resulting signed graph is in general not structurally balanced. It is shown in the paper that the decision-making process is affected by the frustration of the signed graph, in the sense that a nontrivial decision can be reached only if the social commitment of the agents is high enough to win the disorder introduced by the frustration in the network. The higher the frustration of the graph, the higher the commitment strength required from the agents., Funding Agencies|Swedish Research Council [2015-04390]

This paper describes an Arduino Due based platform for digital signal processing (DSP) education. The platform consists of an in-house developed shield for robust interfacing with analog audio signals and user inputs, and an off-the-shelf Arduino Due that executes the students DSP code. This combination enables direct use of the Arduino integrated development environment (IDE), with its low barrier to entry for students, its low maintenance need and cross platform interoperability, and its large user base. Relevant hardware and software features of the platform are discussed throughout, as are design choices made in relation to learning objectives, and the planned use of the platform in our own DSP course., Funding Agencies|School or Electrical Engineering at the KTH Royal Institute of Technology

The existence of market power is a serious concern in modern electric energy markets. Systems and processes that monitor the trading is needed and much research and many proposals on how to deal with the problem have been introduced over the past couple of years. A challenge to all such methods is execution speed, since the identification of market power needs to be done in real-time as market prices are settled. To overcome this challenge the methods need to be simple, involving limited computational effort. This paper presents a simple method with the potential of overcoming the challenge of execution speed. The method is based on the idea of determining the participants with the ability to make considerable increases in price raises without losing all market shares. Such determination can be made in advance during day ahead trading to identify critical areas. During realtime settlement, the method can then be used in a second iteration to study the identified critical areas. In the paper we propose a way to calculate the remaining market shares after a large price raise and refer to these as Monopolistic Energy Levels. These calculated levels of energy, that are deliverable by a single certain participant or by a certain group of participants, are caused by the active congestions in the network. The method detects the quantity of these energy levels and their respective locations in the network. This is a prospective method when used with a prediction of the following days demand. The paper presents the background and development of the proposed method. The use of AC and DC power flow as means to determine the monopolistic energy levels are analyzed and discussed. The paper is concluded with examples of application of the method to a simple multi area power system.

Operationally efficient radio networks typically feature a high degree of self-organization. This means less planning efforts and manual intervention, and a potential for better radio resource utilization when network elements adapts its operation to the observed local conditions. The focus in this paper is selfoptimization of the random access channel (RACH) in the 3G Long Term Evolution (LTE). A comprehensive tutorial about the RACH procedure is provided to span the complexity of the selfoptimization. Moreover, the paper addresses RACH key performance metrics and appropriate modeling of the various steps and components of the procedure. Finally, some coupling between parameters and key performance metrics as well as selfoptimization examples are presented together with a feasibility discussion. The main ambition with this workshop paper is to present and define a relevant set of self-optimization problems, rather than to provide a complete solution.

Radio network management simplification concerns to some extent the removal, not the simplification, of tasks. In this paper we present an approach for automatic network management in 3G long term evolution (LTE), namely, methods for automatic configuration of locally-unique physical cell identities and neighbor cell relation lists. We show that these issues can be removed from the list of planning tasks and completely replaced by autonomous algorithms. These algorithms make use of mobile measurements to detect local cell identity conflicts, resolve them, and to update the neighbor cell relation lists in the cells. The performance of the approach is determined using simulations of realistically deployed macro networks. The simulations illustrate the ability of the algorithms to resolve local cell identity conflicts. In particular, the algorithms are capable of both accommodating new cells and handling a worst case scenario where all cells are initiated with the same local cell identities and where neighbor cell relation lists are empty. The contributions in this paper are meant to aid operators by allowing them to replace time consuming and costly tasks with automatic mechanisms, thus, reducing operational expenditure.

This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance., Funding Agencies|CNPq - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; CISB - Centro de Pesquisa e Inovacao Sueco-Brasileiro and Saab AB; Vinnova Industry Excellence Center ELLIIT at Linkoping University

This paper considers the input design of kernelbased regularization methods for LTI system identification. We first derive the Bayesian mean squared error matrix under the Bayesian perspective, and then use some typical scalar measures (e.g., the A-optimality, D-optimality, and E-optimality) as optimization criteria for the input design problem. Instead of directly solving the nonconvex optimization problem, we propose a two-step procedure. The first step is to solve a convex optimization and the second one is to determine the inverse image of a quadratic map. Both of these two steps can be solved efficiently by the proposed method and hence all the globally optimal inputs are found. In particular, we show that for some kernels, the optimal input under the D-optimality has an explicit expression., Funding Agencies|Thousand Youth Talents Plan - central government of China; Shenzhen Projects - Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Chinese University of Hong Kong, Shenzhen [PF. 01.000249, 2014.0003.23]; Swedish Research Council [2014-5894]; NSFC [61773329]

A signed network is a network with each link associated with a positive or negative sign. Models for nodes interacting over such signed networks, where two types of interactions are defined along the positive and negative links, respectively, arise from various biological, social, political, and economical systems. Starting from standard consensus dynamics, there are two basic types of negative interactions along negative links, namely state flipping or relative-state flipping. In this paper, we provide an algebraic-graphical method serving as a systematic tool of studying these dynamics over signed networks. Utilizing generalized Perron-Frobenius theory, graph theory, and elementary algebraic recursions, we show this method is useful to establish a series of basic convergence results for dynamics over signed networks.

In the consensus problem considered in this paper, each agent can impose a lower and an upper bound on the achievable consensus values. We show that if such state constraints are implemented by saturating the value transmitted to the neighboring nodes, the resulting constrained consensus problem must converge to the intersection of the intervals imposed by the individual agents., Funding Agencies|Swedish Research Council [2015-04390]

In this paper, an indoor vehicle multi-level positioning algorithm is proposed that makes use of an indoor map, as well as dead-reckoning sensor information that is available in every car. A particle filter framework is used for online optimal Bayesian vehicle positioning with indoor-outdoor transitions. The method is validated experimentally in two indoor multi-level car parks. The achieved results indicate that accurate indoor positioning is possible already today without relying on expensive technology such as e.g. laser scanners or additional hardware., Funding Agencies|Wallenberg Autonomous System Program (WASP); Swedish Research Council; Excellence Center at Linkoping and Lund in Information Technology (ELLIIT)

This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance., Funding Agencies|CNPq - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; CISB - Centro de Pesquisa e Inovacao Sueco-Brasileiro and Saab AB; Vinnova Industry Excellence Center ELLIIT at Linkoping University

This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance., Funding Agencies|CNPq - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; CISB - Centro de Pesquisa e Inovacao Sueco-Brasileiro and Saab AB; Vinnova Industry Excellence Center ELLIIT at Linkoping University

This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance., Funding Agencies|CNPq - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; CISB - Centro de Pesquisa e Inovacao Sueco-Brasileiro and Saab AB; Vinnova Industry Excellence Center ELLIIT at Linkoping University

This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance., Funding Agencies|CNPq - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico; CISB - Centro de Pesquisa e Inovacao Sueco-Brasileiro and Saab AB; Vinnova Industry Excellence Center ELLIIT at Linkoping University

Advances in sensor systems have resulted in the availability of high resolution sensors, capable of generating massive amounts of data. For complex systems to run online, the primary focus is on computationally efficient filters for the estimation of latent states related to the data. In this paper a novel method for efficient state estimation with the unscented Kalman Filter is proposed. The focus is on applications consisting of a massive amount of data. From a modelling perspective, this amounts to a measurement vector with dimensionality significantly greater than the dimensionality of the state vector. The efficiency of the filter is derived from a parallel filter structure which is enabled by the expectation propagation algorithm. A novel parallel measurement processing expectation propagation unscented Kalman filter is developed. The primary advantage of the novel algorithm is in the ability to achieve computational improvements with negligible loses in filter accuracy. An example of robot localization with a high resolution laser rangefinder sensor is presented. A 47.53% decrease in computational time was exhibited for a scenario with a processing platform consisting of 4 processors, with a negligible loss in accuracy., Funding Agencies|EC Seventh Framework Programme TRAcking in compleX sensor systems (TRAX) [607400]; UK Royal Society fund [E150823]

In order to meet the requirements for autonomous systems in real world applications, reliable path following controllers have to be designed to execute planned paths despite the existence of disturbances and model errors. In this paper we propose a Linear Quadratic controller for stabilizing a 2-trailer system with possible off-axle hitching around preplanned paths in backward motion. The controller design is based on a kinematic model of a general 2-trailer system including the possibility for off-axle hitching. Closed-loop stability is proved around a set of paths, typically chosen to represent the possible output from the path planner, using theory from linear differential inclusions. Using convex optimization tools a single quadratic Lyapunov function is computed for the entire set of paths.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The identification of continuous-time models of dynamical systems based on sampled measurements of input and output signals is a research topic that has received much attention during the past decades. However, a framework for the correct assessment of the performance of various estimation methods, as well as their numerical reliability, is still missing due to a number of benchmarking difficulties, equally applicable to both discrete-and continuous-time identification problems. This paper revisits this topic, reports new numerical results, highlights several fundamental aspects regarding the definition of an appropriate benchmark for the evaluation of continuous-time linear model identification algorithms and discusses several means of addressing the related existing problems., Funding Agencies|ONERA - The French Aerospace Lab; Universite de Lorraine

In this paper, the maximum entropy property of the discrete-time first-order stable spline kernel is studied. The advantages of studying this property in discrete-time domain instead of continuous-time domain are outlined. One of such advantages is that the differential entropy rate is well-defined for discrete-time stochastic processes. By formulating the maximum entropy problem for discrete-time stochastic processes we provide a simple and self-contained proof to show what maximum entropy property the discrete-time first-order stable spline kernel has.

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work., Virtual Photo Studio (VPS)

This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approximate the global sufficient statistics throughout the network. The evaluation of the proposed algorithm is carried out on a Terrain Based Navigation problem where the unknown parameters of the observation noise model contain relevant information about the terrain properties.

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approximate the global sufficient statistics throughout the network. The evaluation of the proposed algorithm is carried out on a Terrain Based Navigation problem where the unknown parameters of the observation noise model contain relevant information about the terrain properties.

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work., Virtual Photo Studio (VPS)

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

This paper presents a method for matching spotlight Synthetic Aperture Radar (SAR) images with a georeferenced 3D-map as means for navigational aid. A hypothesis of the flying platform's absolute position, velocity and direction - which later can be used to correct the inertial navigation system - is attained by image matching and optimization. A projective model with 6 DoF is used to create a simulated SAR image from a 3D map. The parameters of the projective model represents the most important of the platform's navigation state, and these are adjusted by Chamfer matching the captured SAR image to simulated ones. The performance is demonstrated on real spotlight SAR images and 3D-map, and the error is shown to be only a few pixels in average, which in our case is about 3 meters., LINK-SIC

This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approximate the global sufficient statistics throughout the network. The evaluation of the proposed algorithm is carried out on a Terrain Based Navigation problem where the unknown parameters of the observation noise model contain relevant information about the terrain properties.

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work., Virtual Photo Studio (VPS)

In this paper an extension to the sampling based motion planning framework CL-RRT is presented. The framework uses a system model and a stabilizing controller to sample the perceived environment and build a tree of possible trajectories that are evaluated for execution. Complex system models and constraints are easily handled by a forward simulation making the framework widely applicable. To increase operational safety we propose a sampling recovery scheme that performs a deterministic brake profile regeneration using collision information from the forward simulation. This greatly increases the number of safe trajectories and also reduces the number of samples that produce infeasible results. We apply the framework to a Scania G480 mining truck and evaluate the algorithm in a simple yet challenging obstacle course and show that our approach greatly increases the number of feasible paths available for execution., iQMatic

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approximate the global sufficient statistics throughout the network. The evaluation of the proposed algorithm is carried out on a Terrain Based Navigation problem where the unknown parameters of the observation noise model contain relevant information about the terrain properties.

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work., Virtual Photo Studio (VPS)

In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.

System identification with regularization methods has attracted increasing attention recently and is a complement to the current standard maximum likelihood/ prediction error method. In this paper, we focus on the kernel-based regularization method and give a spectral analysis of the so-called diagonal correlated (DC) kernel, one family of kernel structures that has been proven useful for linear time-invariant system identification. In particular, using the theory of Bessel functions, we derive the eigenvalues and corresponding eigenfunctions of the DC kernel. Accordingly, we derive the Karhunen-Loeve expansion of the stochastic process whose covariance function is the DC kernel.

This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approximate the global sufficient statistics throughout the network. The evaluation of the proposed algorithm is carried out on a Terrain Based Navigation problem where the unknown parameters of the observation noise model contain relevant information about the terrain properties.

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work., Virtual Photo Studio (VPS)

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.