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Reinforcement learning techniques have successfully been applied to solve challenging problems. Among the more famous examples are playing games such as Go and real-time computer games such as StarCraft II. In addition, reinforcement learning has successfully been deployed in cyber-physical systems such as robots playing a curling-based game. These are all important and significant achievements indicating that the techniques can be of value for the aerospace industry. However, to use these techniques in the aerospace industry, very high requirements on verification and validation must be met. In this position paper, we outline four key problems for verification and validation of reinforcement learning techniques. Solving these are an important step towards enabling reinforcement learning techniques to be used in safety critical domains such as the aerospace industry., WASP

The new generation laser-based FLASH 3D imaging sensors enable data collection at video rate. This opens up for realtime data analysis but also set demands on the signal processing. In this paper the possibilities and challenges with this new data type are discussed. The commonly used focal plane array based detectors produce range estimates that vary with the target's surface reflectance and target range, and our experience is that the built-in signal processing may not compensate fully for that. We propose a simple adjustment that can be used even if some sensor parameters are not known. The cost for the instantaneous image collection is, compared to scanning laser radar systems, lower range accuracy. By gathering range information from several frames the geometrical information of the target can be obtained. We also present an approach of how range data can be used to remove foreground clutter in front of a target. Further, we illustrate how range data enables target classification in near real-time and that the results can be improved if several frames are co-registered. Examples using data from forest and maritime scenes are shown.

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

Point-to-point MIMO and massive MIMO techniques have played significantroles in the success of 4G and 5G radio networks, and in 6G we believethat distributed MIMO will play a similar critical role. The performanceof downlink phase coherent distributed MIMO transmission relies on tightphase alignment between the serving access points (APs) in the system.In realistic scenarios, there will always be some level of phasemisalignment between APs due to e.g., differences in the local clocksof the APs, which can severely degrade the performance. One main contribution of this paper is that we propose the use of aLinear Quadratic Regulator (LQR) based solution for calculating downlinkprecoding weights in D-MIMO systems. The optimal LQR based precodingsolution is numerically stable and computationally efficient, and it caneasily utilise parallel computing in distributed or centralised hardwareprocessors. Furthermore, we also show how the LQR based solution can bemodified to include differently sized subsets of serving APs for each UE,which enables a scalable tradeoff between performance and complexity. Another main contribution of the paper is that we identify a new phasemisalignment problem in D-MIMO. The proposed LQR-based precoding methodis the first solution that takes not only the channel estimation phaseerrors, but also the relative phase errors between serving APs intoaccount when designing the downlink D-MIMO transmission precoder. By this,some of the performance lost due to different causes of phase misalignmentcan be regained. In the scenarios studied in this paper we observe 20-70%performance increase of the proposed method compared to a reference casewhere residual phase errors are ignored when determining the downlinkprecoding weights., REINDEER project of the European Union's Horizon 2020 research and innovation programme under gran agreement no. 101013425

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

A method for obstacle detection using the sound that a drone naturally emits is proposed. The sound emitted from a vehicle, ego-noise, is often considered a complicating factor for mission fulfilment, without purpose. The idea in this paper is to utilise this ego-noise for obstacle detection, being the first to perform practical experiments of this. Adding a few microphones to the vehicle, the ego-noise is utilised as the sound source for echolocation. The method consists of auto-correlating the received signals to estimate echo delays, using the known array geometry and signal propagation speed to relate delays to distances, and then beamforming to position targets. A proof-of-concept has been constructed, and promising results are presented for experiments in a controlled environment.

Optimal task and motion planning (TAMP) has seen an increase in interest in recent years. In this paper we propose methods for using numerical optimal control to improve upon a feasible solution to a TAMP rearrangement problem. The methods are extensions of existing improvement methods for pure motion planning. The first method poses an optimal control problem (OCP) to simultaneously improve all motions in the plan. The second method, which we denote multiple finite horizons (MFH), takes inspiration from finite horizon control and poses a sequence of finite horizon OCPs involving variables for the positions of temporary placements of movable objects as well as motions in the plan, such that after solving each problem a feasible plan is maintained and the plan cost is non-increasing after each step. The methods are evaluated on a TAMP problem for tractor-trailers in numerical experiments, and the results show that both methods improve the plan for the evaluated problems. The results also show that MFH can reduce the computation time compared to the first method, and that on one example problem it achieves plans of similar or better quality as when all the motions are optimized at the same time provided that the horizon length is sufficiently long.

A method for obstacle detection using the sound that a drone naturally emits is proposed. The sound emitted from a vehicle, ego-noise, is often considered a complicating factor for mission fulfilment, without purpose. The idea in this paper is to utilise this ego-noise for obstacle detection, being the first to perform practical experiments of this. Adding a few microphones to the vehicle, the ego-noise is utilised as the sound source for echolocation. The method consists of auto-correlating the received signals to estimate echo delays, using the known array geometry and signal propagation speed to relate delays to distances, and then beamforming to position targets. A proof-of-concept has been constructed, and promising results are presented for experiments in a controlled environment.

Optimal task and motion planning (TAMP) has seen an increase in interest in recent years. In this paper we propose methods for using numerical optimal control to improve upon a feasible solution to a TAMP rearrangement problem. The methods are extensions of existing improvement methods for pure motion planning. The first method poses an optimal control problem (OCP) to simultaneously improve all motions in the plan. The second method, which we denote multiple finite horizons (MFH), takes inspiration from finite horizon control and poses a sequence of finite horizon OCPs involving variables for the positions of temporary placements of movable objects as well as motions in the plan, such that after solving each problem a feasible plan is maintained and the plan cost is non-increasing after each step. The methods are evaluated on a TAMP problem for tractor-trailers in numerical experiments, and the results show that both methods improve the plan for the evaluated problems. The results also show that MFH can reduce the computation time compared to the first method, and that on one example problem it achieves plans of similar or better quality as when all the motions are optimized at the same time provided that the horizon length is sufficiently long.

A method for obstacle detection using the sound that a drone naturally emits is proposed. The sound emitted from a vehicle, ego-noise, is often considered a complicating factor for mission fulfilment, without purpose. The idea in this paper is to utilise this ego-noise for obstacle detection, being the first to perform practical experiments of this. Adding a few microphones to the vehicle, the ego-noise is utilised as the sound source for echolocation. The method consists of auto-correlating the received signals to estimate echo delays, using the known array geometry and signal propagation speed to relate delays to distances, and then beamforming to position targets. A proof-of-concept has been constructed, and promising results are presented for experiments in a controlled environment.

For virtually every area of science and engineering, state estimation is ubiquitous. Accurate state estimation requires a moderately precise mathematical model of the system, typically based on domain expertise. These models exist for a plethora of applications and available state estimators can generally produce accurate estimates. However, the models usually ignore hard-to-model phenomena, either due to the cost or the difficulty of modeling these characteristics. Further, the most widely used state estimator for nonlinear systems is still the extended Kalman filter (EKF), which may suffer from divergence for complex models, which essentially restricts the complexity of the usable models. Generally speaking, this thesis investigates ways of improving state estimation. Firstly, existing state-space models (SSMs) for target tracking are augmented with a Gaussian process (GP) in order to learn hard-to-model system characteristics online. Secondly, improved linearization-based state estimators are proposed that exhibit favorable robustness properties to the parameters of the noise processes driving the SSM. The first part of the thesis explores joint state estimation and model learning in partially unknown SSMs, where some a priori domain expertise is available, but parts of the model need to be learned online. Paper A combines a linear, a priori identified, SSM with an approximate GP. An EKF is applied to this GP-augmented SSM in order to jointly estimate the state of the system and learn the, a priori, unknown dynamics. This empirically works well and substantially reduces the prediction error of the dynamical model as compared to a non-augmented SSM. Paper B explores ways of reducing the computational complexity of the method of Paper A. Crucially, it uses a compact kernel in the GP, which admits an equivalent basis function (BF) representation where only a few BFs are non-zero at any given system state. This enables a method that is essentially computationally inv, Funding agency: The Wallenberg AI and Autonomous Systems and Software Program (WASP), funded by the Knut and Alice Wallenberg Foundation

Optimal task and motion planning (TAMP) has seen an increase in interest in recent years. In this paper we propose methods for using numerical optimal control to improve upon a feasible solution to a TAMP rearrangement problem. The methods are extensions of existing improvement methods for pure motion planning. The first method poses an optimal control problem (OCP) to simultaneously improve all motions in the plan. The second method, which we denote multiple finite horizons (MFH), takes inspiration from finite horizon control and poses a sequence of finite horizon OCPs involving variables for the positions of temporary placements of movable objects as well as motions in the plan, such that after solving each problem a feasible plan is maintained and the plan cost is non-increasing after each step. The methods are evaluated on a TAMP problem for tractor-trailers in numerical experiments, and the results show that both methods improve the plan for the evaluated problems. The results also show that MFH can reduce the computation time compared to the first method, and that on one example problem it achieves plans of similar or better quality as when all the motions are optimized at the same time provided that the horizon length is sufficiently long.

A method for obstacle detection using the sound that a drone naturally emits is proposed. The sound emitted from a vehicle, ego-noise, is often considered a complicating factor for mission fulfilment, without purpose. The idea in this paper is to utilise this ego-noise for obstacle detection, being the first to perform practical experiments of this. Adding a few microphones to the vehicle, the ego-noise is utilised as the sound source for echolocation. The method consists of auto-correlating the received signals to estimate echo delays, using the known array geometry and signal propagation speed to relate delays to distances, and then beamforming to position targets. A proof-of-concept has been constructed, and promising results are presented for experiments in a controlled environment.

Optimal task and motion planning (TAMP) has seen an increase in interest in recent years. In this paper we propose methods for using numerical optimal control to improve upon a feasible solution to a TAMP rearrangement problem. The methods are extensions of existing improvement methods for pure motion planning. The first method poses an optimal control problem (OCP) to simultaneously improve all motions in the plan. The second method, which we denote multiple finite horizons (MFH), takes inspiration from finite horizon control and poses a sequence of finite horizon OCPs involving variables for the positions of temporary placements of movable objects as well as motions in the plan, such that after solving each problem a feasible plan is maintained and the plan cost is non-increasing after each step. The methods are evaluated on a TAMP problem for tractor-trailers in numerical experiments, and the results show that both methods improve the plan for the evaluated problems. The results also show that MFH can reduce the computation time compared to the first method, and that on one example problem it achieves plans of similar or better quality as when all the motions are optimized at the same time provided that the horizon length is sufficiently long.

For virtually every area of science and engineering, state estimation is ubiquitous. Accurate state estimation requires a moderately precise mathematical model of the system, typically based on domain expertise. These models exist for a plethora of applications and available state estimators can generally produce accurate estimates. However, the models usually ignore hard-to-model phenomena, either due to the cost or the difficulty of modeling these characteristics. Further, the most widely used state estimator for nonlinear systems is still the extended Kalman filter (EKF), which may suffer from divergence for complex models, which essentially restricts the complexity of the usable models. Generally speaking, this thesis investigates ways of improving state estimation. Firstly, existing state-space models (SSMs) for target tracking are augmented with a Gaussian process (GP) in order to learn hard-to-model system characteristics online. Secondly, improved linearization-based state estimators are proposed that exhibit favorable robustness properties to the parameters of the noise processes driving the SSM. The first part of the thesis explores joint state estimation and model learning in partially unknown SSMs, where some a priori domain expertise is available, but parts of the model need to be learned online. Paper A combines a linear, a priori identified, SSM with an approximate GP. An EKF is applied to this GP-augmented SSM in order to jointly estimate the state of the system and learn the, a priori, unknown dynamics. This empirically works well and substantially reduces the prediction error of the dynamical model as compared to a non-augmented SSM. Paper B explores ways of reducing the computational complexity of the method of Paper A. Crucially, it uses a compact kernel in the GP, which admits an equivalent basis function (BF) representation where only a few BFs are non-zero at any given system state. This enables a method that is essentially computationally inv, Funding agency: The Wallenberg AI and Autonomous Systems and Software Program (WASP), funded by the Knut and Alice Wallenberg Foundation

For virtually every area of science and engineering, state estimation is ubiquitous. Accurate state estimation requires a moderately precise mathematical model of the system, typically based on domain expertise. These models exist for a plethora of applications and available state estimators can generally produce accurate estimates. However, the models usually ignore hard-to-model phenomena, either due to the cost or the difficulty of modeling these characteristics. Further, the most widely used state estimator for nonlinear systems is still the extended Kalman filter (EKF), which may suffer from divergence for complex models, which essentially restricts the complexity of the usable models. Generally speaking, this thesis investigates ways of improving state estimation. Firstly, existing state-space models (SSMs) for target tracking are augmented with a Gaussian process (GP) in order to learn hard-to-model system characteristics online. Secondly, improved linearization-based state estimators are proposed that exhibit favorable robustness properties to the parameters of the noise processes driving the SSM. The first part of the thesis explores joint state estimation and model learning in partially unknown SSMs, where some a priori domain expertise is available, but parts of the model need to be learned online. Paper A combines a linear, a priori identified, SSM with an approximate GP. An EKF is applied to this GP-augmented SSM in order to jointly estimate the state of the system and learn the, a priori, unknown dynamics. This empirically works well and substantially reduces the prediction error of the dynamical model as compared to a non-augmented SSM. Paper B explores ways of reducing the computational complexity of the method of Paper A. Crucially, it uses a compact kernel in the GP, which admits an equivalent basis function (BF) representation where only a few BFs are non-zero at any given system state. This enables a method that is essentially computationally inv, Funding agency: The Wallenberg AI and Autonomous Systems and Software Program (WASP), funded by the Knut and Alice Wallenberg Foundation

For virtually every area of science and engineering, state estimation is ubiquitous. Accurate state estimation requires a moderately precise mathematical model of the system, typically based on domain expertise. These models exist for a plethora of applications and available state estimators can generally produce accurate estimates. However, the models usually ignore hard-to-model phenomena, either due to the cost or the difficulty of modeling these characteristics. Further, the most widely used state estimator for nonlinear systems is still the extended Kalman filter (EKF), which may suffer from divergence for complex models, which essentially restricts the complexity of the usable models. Generally speaking, this thesis investigates ways of improving state estimation. Firstly, existing state-space models (SSMs) for target tracking are augmented with a Gaussian process (GP) in order to learn hard-to-model system characteristics online. Secondly, improved linearization-based state estimators are proposed that exhibit favorable robustness properties to the parameters of the noise processes driving the SSM. The first part of the thesis explores joint state estimation and model learning in partially unknown SSMs, where some a priori domain expertise is available, but parts of the model need to be learned online. Paper A combines a linear, a priori identified, SSM with an approximate GP. An EKF is applied to this GP-augmented SSM in order to jointly estimate the state of the system and learn the, a priori, unknown dynamics. This empirically works well and substantially reduces the prediction error of the dynamical model as compared to a non-augmented SSM. Paper B explores ways of reducing the computational complexity of the method of Paper A. Crucially, it uses a compact kernel in the GP, which admits an equivalent basis function (BF) representation where only a few BFs are non-zero at any given system state. This enables a method that is essentially computationally inv, Funding agency: The Wallenberg AI and Autonomous Systems and Software Program (WASP), funded by the Knut and Alice Wallenberg Foundation

This paper presents a method to certify the computational complexity of a standard Branch and Bound method for solving Mixed-Integer Quadratic Programming (MIQP) problems defined as instances of a multi-parametric MIQP. Beyond previous work, not only the size of the binary search tree is considered, but also the exact complexity of solving the relaxations in the nodes by using recent results from exact complexity certification of active-set QP methods. With the algorithm proposed in this paper, a total worst-case number of QP iterations to be performed in order to solve the MIQP problem can be determined as a function of the parameter in the problem. An important application of the proposed method is Model Predictive Control for hybrid systems, that can be formulated as an MIQP that has to be solved in real-time. The usefulness of the proposed method is successfully illustrated in numerical examples., Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

This paper presents a method to certify the computational complexity of a standard Branch and Bound method for solving Mixed-Integer Quadratic Programming (MIQP) problems defined as instances of a multi-parametric MIQP. Beyond previous work, not only the size of the binary search tree is considered, but also the exact complexity of solving the relaxations in the nodes by using recent results from exact complexity certification of active-set QP methods. With the algorithm proposed in this paper, a total worst-case number of QP iterations to be performed in order to solve the MIQP problem can be determined as a function of the parameter in the problem. An important application of the proposed method is Model Predictive Control for hybrid systems, that can be formulated as an MIQP that has to be solved in real-time. The usefulness of the proposed method is successfully illustrated in numerical examples., Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

This paper presents a method to certify the computational complexity of a standard Branch and Bound method for solving Mixed-Integer Quadratic Programming (MIQP) problems defined as instances of a multi-parametric MIQP. Beyond previous work, not only the size of the binary search tree is considered, but also the exact complexity of solving the relaxations in the nodes by using recent results from exact complexity certification of active-set QP methods. With the algorithm proposed in this paper, a total worst-case number of QP iterations to be performed in order to solve the MIQP problem can be determined as a function of the parameter in the problem. An important application of the proposed method is Model Predictive Control for hybrid systems, that can be formulated as an MIQP that has to be solved in real-time. The usefulness of the proposed method is successfully illustrated in numerical examples., Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

This paper presents a method to certify the computational complexity of a standard Branch and Bound method for solving Mixed-Integer Quadratic Programming (MIQP) problems defined as instances of a multi-parametric MIQP. Beyond previous work, not only the size of the binary search tree is considered, but also the exact complexity of solving the relaxations in the nodes by using recent results from exact complexity certification of active-set QP methods. With the algorithm proposed in this paper, a total worst-case number of QP iterations to be performed in order to solve the MIQP problem can be determined as a function of the parameter in the problem. An important application of the proposed method is Model Predictive Control for hybrid systems, that can be formulated as an MIQP that has to be solved in real-time. The usefulness of the proposed method is successfully illustrated in numerical examples., Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

This paper presents a method to certify the computational complexity of a standard Branch and Bound method for solving Mixed-Integer Quadratic Programming (MIQP) problems defined as instances of a multi-parametric MIQP. Beyond previous work, not only the size of the binary search tree is considered, but also the exact complexity of solving the relaxations in the nodes by using recent results from exact complexity certification of active-set QP methods. With the algorithm proposed in this paper, a total worst-case number of QP iterations to be performed in order to solve the MIQP problem can be determined as a function of the parameter in the problem. An important application of the proposed method is Model Predictive Control for hybrid systems, that can be formulated as an MIQP that has to be solved in real-time. The usefulness of the proposed method is successfully illustrated in numerical examples., Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

The use of the CDIO framework in the development of the ECIU University is presented. The paper discusses the relatively moderate adaptations and modifications of the CDIO Syllabus and Standards that are necessary to make the documents applicable also in this context. Since challenge-based learning (CBL) is central learning format in the ECIU University, special attention is given to the connections between CBL method, the conceive-design-implement-operate sequence and project-based learning, which is central in the CDIO framework. The paper presents both general aspects and examples of the applications and activities within ECIU University and Linköping University (LiU). The main messages of the paper are that the development of the ECIU University will benefit from applying the CDIO framework since it offers references for what an education should give, in terms of knowledge and skills, and how an education program should be designed. In addition, the components of the CDIO framework require a moderate amount of adaptation to be directly applicable. Examples of the ongoing implementation activities at LiU.

The use of the CDIO framework in the development of the ECIU University is presented. The paper discusses the relatively moderate adaptations and modifications of the CDIO Syllabus and Standards that are necessary to make the documents applicable also in this context. Since challenge-based learning (CBL) is central learning format in the ECIU University, special attention is given to the connections between CBL method, the conceive-design-implement-operate sequence and project-based learning, which is central in the CDIO framework. The paper presents both general aspects and examples of the applications and activities within ECIU University and Linköping University (LiU). The main messages of the paper are that the development of the ECIU University will benefit from applying the CDIO framework since it offers references for what an education should give, in terms of knowledge and skills, and how an education program should be designed. In addition, the components of the CDIO framework require a moderate amount of adaptation to be directly applicable. Examples of the ongoing implementation activities at LiU.

The use of the CDIO framework in the development of the ECIU University is presented. The paper discusses the relatively moderate adaptations and modifications of the CDIO Syllabus and Standards that are necessary to make the documents applicable also in this context. Since challenge-based learning (CBL) is central learning format in the ECIU University, special attention is given to the connections between CBL method, the conceive-design-implement-operate sequence and project-based learning, which is central in the CDIO framework. The paper presents both general aspects and examples of the applications and activities within ECIU University and Linköping University (LiU). The main messages of the paper are that the development of the ECIU University will benefit from applying the CDIO framework since it offers references for what an education should give, in terms of knowledge and skills, and how an education program should be designed. In addition, the components of the CDIO framework require a moderate amount of adaptation to be directly applicable. Examples of the ongoing implementation activities at LiU.

The use of the CDIO framework in the development of the ECIU University is presented. The paper discusses the relatively moderate adaptations and modifications of the CDIO Syllabus and Standards that are necessary to make the documents applicable also in this context. Since challenge-based learning (CBL) is central learning format in the ECIU University, special attention is given to the connections between CBL method, the conceive-design-implement-operate sequence and project-based learning, which is central in the CDIO framework. The paper presents both general aspects and examples of the applications and activities within ECIU University and Linköping University (LiU). The main messages of the paper are that the development of the ECIU University will benefit from applying the CDIO framework since it offers references for what an education should give, in terms of knowledge and skills, and how an education program should be designed. In addition, the components of the CDIO framework require a moderate amount of adaptation to be directly applicable. Examples of the ongoing implementation activities at LiU.

The use of the CDIO framework in the development of the ECIU University is presented. The paper discusses the relatively moderate adaptations and modifications of the CDIO Syllabus and Standards that are necessary to make the documents applicable also in this context. Since challenge-based learning (CBL) is central learning format in the ECIU University, special attention is given to the connections between CBL method, the conceive-design-implement-operate sequence and project-based learning, which is central in the CDIO framework. The paper presents both general aspects and examples of the applications and activities within ECIU University and Linköping University (LiU). The main messages of the paper are that the development of the ECIU University will benefit from applying the CDIO framework since it offers references for what an education should give, in terms of knowledge and skills, and how an education program should be designed. In addition, the components of the CDIO framework require a moderate amount of adaptation to be directly applicable. Examples of the ongoing implementation activities at LiU.

Accurate localization is a part of most autonomous systems. GNSS is today the go to solution for localization but is unreliable due to jamming and is not available indoors. Inertial navigation aided by visual measurements, e.g., optical flow, offers an alternative. Traditional feature-based optical flow is limited to scenes with good features, current development of deep neural network derived dense optical flow is an interesting alternative. This paper proposes a method to evaluate the result of dense optical flow on real image sequences using traditional feature-based optical flow and uses this to compare six different dense optical flow methods. The results of the dense methods are promising, and no clear winner amongst the methods can be determined. The results are discussed in the context of how they can be used to support localization.

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.

A case study of the use of reflections within the Applied physics and electrical engineering program at Linköping University is presented. Reflections have been used for several years and they are done at four stages in the program, in terms of reflections at the end of the Introductory course in year one, design-implement experiences in year three and five, and a reflection document that is the last component of the Master’s thesis. In the first three stages a project model is used to support the planning and execution of the project, and in the project model the project work ends with a reflection. In the reflection document connected to the Master’s thesis the student reflects upon both the thesis work itself and the entire education program, according to the sections and subsections of the CDIO Syllabus. The paper describes how the reflections are integrated in the program. Experiences from student perspective are collected in a small-scale study via interviews with students from year one and year five.

Accurate localization is a part of most autonomous systems. GNSS is today the go to solution for localization but is unreliable due to jamming and is not available indoors. Inertial navigation aided by visual measurements, e.g., optical flow, offers an alternative. Traditional feature-based optical flow is limited to scenes with good features, current development of deep neural network derived dense optical flow is an interesting alternative. This paper proposes a method to evaluate the result of dense optical flow on real image sequences using traditional feature-based optical flow and uses this to compare six different dense optical flow methods. The results of the dense methods are promising, and no clear winner amongst the methods can be determined. The results are discussed in the context of how they can be used to support localization.

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.

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications., Funding Agencies|European Research Council; Amsterdam Institute for Advanced Metropolitan Solutions through the ALLEGRO [669792]; Center for Industrial Information Technology at Linkoeping University (CENIIT) [17.12]

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications., Funding Agencies|European Research Council; Amsterdam Institute for Advanced Metropolitan Solutions through the ALLEGRO [669792]; Center for Industrial Information Technology at Linkoeping University (CENIIT) [17.12]

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications., Funding Agencies|European Research Council; Amsterdam Institute for Advanced Metropolitan Solutions through the ALLEGRO [669792]; Center for Industrial Information Technology at Linkoeping University (CENIIT) [17.12]

This paper investigates the impact of addition/removal/reweighting of edges in a complex networked linear control system. For networks of positive edge weights, we show that when adding edges leads to the creation of new cycles, these in turn may lead to instabilities. Dynamically, these cycles correspond to positive feedback loops. Conditions are provided under which the modified network is guaranteed to be stable. These conditions are related to the steady state value of the transfer function matrix of the newly created positive feedbacks. The tools we develop in the paper can be used to investigate the fragility of a network, i.e., its robustness to structured perturbations.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)., Funding Agencies|Swedish Research Council [2020-03701]

This paper investigates the impact of addition/removal/reweighting of edges in a complex networked linear control system. For networks of positive edge weights, we show that when adding edges leads to the creation of new cycles, these in turn may lead to instabilities. Dynamically, these cycles correspond to positive feedback loops. Conditions are provided under which the modified network is guaranteed to be stable. These conditions are related to the steady state value of the transfer function matrix of the newly created positive feedbacks. The tools we develop in the paper can be used to investigate the fragility of a network, i.e., its robustness to structured perturbations.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)., Funding Agencies|Swedish Research Council [2020-03701]

This paper deals with distributed Bayesian stateestimation of generally nonlinear stochastic dynamic systems. In particular, distributed point-mass filter algorithm is developed. It is comprised of a basic part that is accurate but data intense and optional step employing advanced copula theory. The optional step significantly reduces data transfer for the price of a small accuracy decrease. In the end, the developed algorithm is numerically compared to the usually employed distributed extended Kalman filter., Funding: project Improving the Quality of Internal Grant Schemes at the UWB [CZ.02.2.69/0.0/0.0/19 073/0016931, SGS-2022-022]; Industry Excellence Center LINK-SIC - VINNOVA; Saab AB

This paper deals with distributed Bayesian stateestimation of generally nonlinear stochastic dynamic systems. In particular, distributed point-mass filter algorithm is developed. It is comprised of a basic part that is accurate but data intense and optional step employing advanced copula theory. The optional step significantly reduces data transfer for the price of a small accuracy decrease. In the end, the developed algorithm is numerically compared to the usually employed distributed extended Kalman filter., Funding: project Improving the Quality of Internal Grant Schemes at the UWB [CZ.02.2.69/0.0/0.0/19 073/0016931, SGS-2022-022]; Industry Excellence Center LINK-SIC - VINNOVA; Saab AB

This paper investigates the impact of addition/removal/reweighting of edges in a complex networked linear control system. For networks of positive edge weights, we show that when adding edges leads to the creation of new cycles, these in turn may lead to instabilities. Dynamically, these cycles correspond to positive feedback loops. Conditions are provided under which the modified network is guaranteed to be stable. These conditions are related to the steady state value of the transfer function matrix of the newly created positive feedbacks. The tools we develop in the paper can be used to investigate the fragility of a network, i.e., its robustness to structured perturbations.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)., Funding Agencies|Swedish Research Council [2020-03701]

A case study of the use of reflections within the Applied physics and electrical engineering program at Linköping University is presented. Reflections have been used for several years and they are done at four stages in the program, in terms of reflections at the end of the Introductory course in year one, design-implement experiences in year three and five, and a reflection document that is the last component of the Master’s thesis. In the first three stages a project model is used to support the planning and execution of the project, and in the project model the project work ends with a reflection. In the reflection document connected to the Master’s thesis the student reflects upon both the thesis work itself and the entire education program, according to the sections and subsections of the CDIO Syllabus. The paper describes how the reflections are integrated in the program. Experiences from student perspective are collected in a small-scale study via interviews with students from year one and year five.

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., Zenith

Accurate localization is a part of most autonomous systems. GNSS is today the go to solution for localization but is unreliable due to jamming and is not available indoors. Inertial navigation aided by visual measurements, e.g., optical flow, offers an alternative. Traditional feature-based optical flow is limited to scenes with good features, current development of deep neural network derived dense optical flow is an interesting alternative. This paper proposes a method to evaluate the result of dense optical flow on real image sequences using traditional feature-based optical flow and uses this to compare six different dense optical flow methods. The results of the dense methods are promising, and no clear winner amongst the methods can be determined. The results are discussed in the context of how they can be used to support localization.

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.

A case study of the use of reflections within the Applied physics and electrical engineering program at Linköping University is presented. Reflections have been used for several years and they are done at four stages in the program, in terms of reflections at the end of the Introductory course in year one, design-implement experiences in year three and five, and a reflection document that is the last component of the Master’s thesis. In the first three stages a project model is used to support the planning and execution of the project, and in the project model the project work ends with a reflection. In the reflection document connected to the Master’s thesis the student reflects upon both the thesis work itself and the entire education program, according to the sections and subsections of the CDIO Syllabus. The paper describes how the reflections are integrated in the program. Experiences from student perspective are collected in a small-scale study via interviews with students from year one and year five.

This paper addresses the trade-off between time and energy-efficiency for the problem of loading and unloading a ship. Container height constraints and energy consumption and regeneration are dealt with. We build upon a previous work that introduced a coordinate system suitable to deal with container avoidance constraints and incorporate the energy related modeling. In addition to changing the coordinate system, standard epigraph reformulations result in an optimal control problem with improved numerical properties. The trade-of is dealt with through the use of weighting of the total time and energy consumption in the cost function. An illustrative example is provided, demonstrating that the energy consumption can be substantially reduced while retaining approximately the same loading time., Funding Agencies|VINNOVA Competence Center LINK-SIC