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This paper describes the deduction of pulse arrival times from digital waveforms recorded with a multichannel data-acquisition (DAQ) system. A linear rise approximation (LRA) arrival timing method provides restricted timing resolution for pulses with nonlinear rise. It reaches 1/20th of the sampling period, if the relation between signal shaping and sampling rate is optimized. We introduce a nonlinear rise approximation (nLRA), which reduces the sampling phase error (SPE) down to less than 1/100th of the sampling period. The proposed timing algorithm uses a single free parameter that can easily be adjusted for various radiation detectors. The technique permits using a rather slow pulse shaping and low sampling rates, thus strongly reducing power consumption and the costs of the system. A high-density DAQ system integrating over 2000 channels inside an OpenVPX crate is presented. A prototype has been tested in the proton beam at cooler synchrotron (COSY) at Jülich Research Center (Germany). [ABSTRACT FROM AUTHOR]

This paper addresses uncertainty quantification of electromagnetic devices determined by the eddy current problem. The multilevel Monte Carlo (MLMC) method is used for the treatment of uncertain parameters while the devices are discretized in space by the finite element method. Both methods yield numerical approximations such that the total error is split into stochastic and spatial contributions. We propose a particular implementation where the spatial error is controlled based on a Richardson extrapolation-based error indicator. The stochastic error, in turn, is efficiently reduced in the MLMC approach by distributing the samples on multiple grids. The method is applied to a toy problem with closed-form solution and to a permanent magnet synchronous machine with uncertainties. The uncertainties under consideration are related to the material properties in the stator and the magnets in the rotor. The examples show that the error indicator works reliably, the meshes used for the different levels do not have to be nested, and, most importantly, MLMC reduces the computational cost by at least one order of magnitude compared to standard Monte Carlo. [ABSTRACT FROM AUTHOR]

Consider a Gaussian memoryless multiple source (GMMS) with $m$ components with joint probability distribution known only to lie in a given class of distributions. A subset of $k \leq m$ components is sampled and compressed with the objective of reconstructing all the $m$ components within a specified level of distortion under a mean-squared error criterion. In Bayesian and nonBayesian settings, the notion of universal sampling rate-distortion function for Gaussian sources is introduced to capture the optimal tradeoffs among sampling, compression rate, and distortion level. Single-letter characterizations are provided for the universal sampling rate-distortion function. Our achievability proofs highlight the following structural property: it is optimal to compress and reconstruct first the sampled components of the GMMS alone, and then form estimates for the unsampled components based on the former. [ABSTRACT FROM AUTHOR]

Polynomial interpolation is widely used in channel modeling for aerospace tracking, telemetry, and command channel simulators, to accurately regenerate the satellite motion parameters with a high sampling rate from the available low sampling rate motion data. In this paper, a novel piecewise osculating interpolation approach is proposed to improve modeling precision. It uses the available distance and its higher-order derivatives at each pair of adjacent knots to construct the piecewise interpolation polynomial, thus achieving a better precision and continuity of the resulting motion parameters. Moreover, it is efficiently implemented with an interpolation filter based on a modified Farrow structure, and the designed filter can interpolate the motion data obtained both through uniform or nonuniform sampling. Additionally, a sampling optimization algorithm is proposed. It optimizes the motion data sampling by minimizing the maximum interpolation error given the average sampling interval and interpolation polynomial order, and therefore, improves the precision further without increasing the costs. The validity of the proposed approach is verified by numerical simulation, and its advantages are demonstrated by comparison with existing methods. [ABSTRACT FROM AUTHOR]

The fault resistance and infeeded currents from tapped transmission lines are the two main challenges to establish correct fault location estimation in multi-terminal transmission lines. Also, fault location estimation in double-circuit lines is more complicated than single-circuit lines due to the effect of mutual coupling and inter-circuit faults. This paper proposes a negative-sequence network-based method to estimate the fault location of double-circuit multi-terminal transmission lines. In the presented method, signals are measured in the relay location and then, communication links are not required as it uses only one end measurements. Also, in conventional distance relays, six elements are needed to calculate the fault location in all types of faults. Furthermore, in this method, by using only one element for each transmission line, accurate fault location can be estimated. Also, high fault resistance, mutual coupling, and infeeded currents from tapped transmission lines do not have impact on the performance of the presented scheme. To investigate the effects of the line shunt capacitances on the proposed method, a PI model of the transmission lines is used in all the investigations. Simulation results carried out using MATLAB software indicate that the proposed scheme is able to produce excellent performance for the fault location estimation. [ABSTRACT FROM AUTHOR]

This correspondence paper aims at addressing the estimation of carrier frequency offset (CFO) for the uplink orthogonal frequency-division multiple access systems. Since the CFOs of the signals from different active users are sparsely distributed in the frequency domain, a sparse Bayesian learning (SBL) is tailored to determine the CFO in this paper, ending up with the SBL assisted CFO (SBL-CFO) estimator. In particular, the CFO estimation problem is first formulated as a sparse nonnegative least squares (S-NNLS) problem. Meanwhile, background noise and sampling errors are mitigated utilizing a selection matrix and a whitening filter, respectively. This enables us to exploit the SBL with nonnegative Laplace prior (SBL-NLP) to solve the S-NNLS problem. Furthermore, in order to make the convergence of the SBL-NLP algorithm faster and its estimation more accurate, the hyperprior inherent in the SBL-NLP algorithm is initialized by the traditional SBL with nonnegative Gaussian prior. Simulation results show that our proposed SBL-CFO estimator significantly outperforms the state-of-the-art estimators in terms of estimation accuracy, especially when the CFOs and the number of active users are large. [ABSTRACT FROM AUTHOR]

The interpolation based on prediction-error filter (PEF) is one of the most effective approaches recently proposed for seismic data reconstruction. However, the number of effective regression equations for estimating the filter coefficients will be much less when missing many seismic traces, which makes the estimated filter coefficients inaccurate or even impossible to be estimated. To improve the accuracy of filter coefficients, in this paper, we design a multiscale and multidirectional PEF, in which the number of effective regression equations can be increased much more, and use it to seismic data reconstruction. First, we estimate the adaptive different directional PEFs using the known data in different scales. The known data can be regularly sampled with randomly or regularly missing, or even both of them. Then, we interpolate missing seismic traces using estimated PEF and the sparse known traces. The regularization in least-squares inversion controls the adaptivity of multiscale multidirectional PEF. The use of more effective regression equations in inversion makes the filter coefficients more accurate. In addition, the multiscale filter can conveniently deal with the case of the simultaneous existence of randomly and regularly missing, while the conventional methods have to be treated separately for randomly and regularly missing. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples. [ABSTRACT FROM AUTHOR]

To solve the problem of poor robustness due to the high dependence of model predictive control (MPC) on the three-phase inverter model, the influence mechanism of the mismatch of circuit parameters R and L, the model discretization error, and sampling errors on MPC prediction error are analyzed in this paper. A feedback self-correction is proposed, which the next current errors are predicted and used to modify the model, based on the previous prediction errors. This proposed method involves simple structure and a small amount of calculation. Steady-state effects of the proposed method are quantized to prove a good inhibitory ability on multifactorial prediction errors. Finally, experimental results in a three-phase inverter are presented to support the theoretical analysis and demonstrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The current reconstruction strategies using single current sensor in the motor drives are widely researched. However, the sampled currents do not represent the average values within the modulation period, especially for the asymmetric modulation or asymmetric sampling solutions. This is due to the following two reasons: 1) The detected current values obtained at different sampling points are utilized to calculate the three-phase currents; and 2) the detected instantaneous currents are applied to estimate the average current values. In this letter, the current change rates under different action vectors are estimated to compensate these errors in a three-phase four-switch interior permanent magnet synchronous motor (IPMSM) drive with single current sensor. The effectiveness of the proposed strategy is verified by experimental results on a 5-kW IPMSM motor prototype, which shows that the current reconstruction errors are reduced after compensation. [ABSTRACT FROM AUTHOR]

In this paper, heading reference-assisted pose estimation (HRPE) has been proposed to compensate inherent drift of visual odometry (VO) on ground vehicles, where an estimation error is prone to grow while the vehicle is making turns or in environments with poor features. By introducing a particular orientation as “heading reference,” a pose estimation framework has been presented to incorporate measurements from heading reference sensors into VO. A graph formulation is then proposed to represent the pose estimation problem under the commonly used graph optimization model. Simulations and experiments on KITTI data set and our self-collected sequences have been conducted to verify the accuracy and robustness of the proposed scheme. KITTI sequences and manually generated heading measurement with Gaussian noises are used in simulation, where rotational drift error is observed to be bounded. Compared with a pure VO, the proposed approach greatly reduces average translational localization error from 153.85 to 24.29 m and 23.80 m in self-collected stereo visual sequences with traveling distance over 4.5 km at the processing rates of 19.7 and 11.1 Hz, for the loosely coupled and tightly coupled models, respectively. Note to Practitioners—When the Global Positioning System is not available or reliable, visual odometry (VO) on ground vehicles is an efficient tool for estimating the pose, which involves translation and rotation. However, VO inherently suffers from drifting issue due to constant iterations. By adding a low-cost heading reference sensor, this paper first introduces graph optimization formulation of pose estimation and then presents a pose estimation framework which incorporates heading measurements to VO such that long-term translation and rotation estimation errors can be greatly reduced in real-time computation. As a supplementary to VO, performance may still deteriorate in environments with poor illumination conditions and high-frequency movements. The proposed approach may be further improved by fusing heading measurements from more sensors or being used to build a heading reference-assisted simultaneous localization and mapping (SLAM) system on any off-the-shelf SLAM framework. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the state of a linear time-invariant Gaussian system using $N$ sensors, where a subset of the sensors can potentially be compromised by an adversary. In this case, locating the compromised sensors is of crucial importance for obtaining an accurate state estimate. Inspired by the clustering algorithm in machine learning, we propose a Gaussian-mixture-model-based (GMM-based) detection mechanism. It clusters the local state estimate autonomously and provides a belief for each sensor, based on which measurements from different sensors can be fused accordingly. When a subset of the sensors are under the optimal innovation-based deception attacks, we derive the remote estimation error covariance recursions under different detection mechanisms, e.g., distributed $\chi ^2$ false-data detector, centralized $\chi ^2$ false-data detector, and GMM-based detection algorithm. The performance of the proposed GMM-based detection algorithm is further evaluated through average belief in the same attack scenario. Moreover, we discuss applications of GMM-based detection algorithm on other attack scenarios, e.g., false-data injection attack, replay attack, and $\epsilon$ -stealthy attack. Simulation examples are provided to demonstrate the developed results. [ABSTRACT FROM AUTHOR]

This paper analyzes the tracking performance of the family of adaptive filtering algorithms based on minimizing the ${{2}}L$ -th moment of the estimation error, with $L$ being an integer greater than 1. The analysis is done for a Markov plant without the usually used assumption of small degree of non-stationarity (DNS). General $L$ and DNS are considered in the analysis. Dependence of the minimum steady-state mean square weight deviation on the values of $L$ , DNS and type of the noise distribution is studied. A measure for evaluating the steady-state stability of the algorithm is introduced. Dependence of the steady-state stability on the values of $L$ , DNS, and type of the noise distribution is studied. Some results are surprising. Theoretical results are supported by simulations. [ABSTRACT FROM AUTHOR]

Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. [ABSTRACT FROM AUTHOR]

This letter proposes a structured approach for accurate estimation of diode reverse-recovery characteristics using datasheet specifications. Considering a conductance model of a diode, the entire reverse-recovery process is divided into four operating modes. The state equations for each mode are derived taking the circuit nonidealities, viz., device capacitance and parasitic inductances, into account. Analytical expressions for reverse-recovery charge, reverse-recovery time, and diode current are derived and numerically solved to estimate the diode conductance directly from manufacturer's data. An empirical model is proposed to characterize the effects of temperature variation on the diode conductance. Numerical simulation and experimental results are presented for antiparallel diodes of three MOSFETs having different current and voltage ratings and also for a discrete p-n diode at different junction temperatures. The maximum estimation error with the proposed approach is 5.1% for peak reverse-recovery current and less than 8% for reverse-recovery time, peak voltage, and energy loss. [ABSTRACT FROM AUTHOR]

Fundamental relations between information and estimation have been established in the literature for the continuous time Gaussian and Poisson channels. In this paper, we demonstrate that such relations hold for a much larger family of continuous-time channels. We introduce the family of semi-martingale channels where the channel output is a semi-martingale stochastic process, and the channel input modulates the characteristics of the semi-martingale. For these channels, which includes as a special case the continuous time Gaussian and Poisson models, we establish new representations relating the mutual information between the channel input and output to an optimal causal filtering loss, thereby unifying and considerably extending results from the Gaussian and Poisson settings. Extensions to the setting of mismatched estimation are also presented where the relative entropy between the laws governing the output of the channel under two different input distributions is equal to the cumulative difference between the estimation loss incurred by using the mismatched and optimal causal filters, respectively. The main tool underlying these results is the Doob–Meyer decomposition of a class of sub-martingales. The results in this paper can be viewed as the continuous-time analogues of recent generalizations for relations between information and estimation for discrete-time Lévy channels. [ABSTRACT FROM AUTHOR]

In Gaussian measurement models, the measurements are given by a known function of the unknown parameter vector, contaminated by additive zero-mean Gaussian noise. When the function is linear, the resulting maximum likelihood estimate (MLE) is well-known to be efficient [unbiased, with a mean square estimation error (MSE) matrix attaining the Cramér–Rao lower bound (CRLB)]. However, when the function is nonlinear, the MLE is only asymptotically efficient. The classical derivation of its asymptotic efficiency uses a first-order perturbation analysis, relying on a “small-errors” assumption, which under subasymptotic conditions turns inaccurate, rendering the MLE generally biased and inefficient. Although a more accurate (higher-order) performance analysis for such cases is of considerable interest, the associated derivations are rather involved, requiring cumbersome notations and indexing. Building on the recent assimilation of tensor computations into signal processing literature, we exploit the tensor formulation of higher-order derivatives to derive a tractable formulation of a higher (up to third-) order perturbation analysis, predicting the bias and MSE matrix of the MLE of parameter vectors in general nonlinear models under subasymptotic conditions. We provide explicit expressions depending on the first three derivatives of the nonlinear measurement function, and demonstrate the resulting ability to predict the “efficiency gap” (relative excess MSE beyond the CRLB) in simulation experiments. We also provide MATLAB code for easy computation of our resulting expressions. [ABSTRACT FROM AUTHOR]

The control of automobile electronic throttle (AET) systems is a challenging task owing to multiple nonlinearities, i.e., transmission friction, gear backlash, limp-home spring set, and external load disturbance. In this paper, a practical tracking control scheme of an AET system is developed using a continuous fast nonsingular terminal sliding mode (CFNTSM) technique based on uncertainty observer. By using the prescribed CFNTSM surface and fast terminal sliding mode-based reaching element, the proposed control implementation guarantees the fast error convergence characteristic and high tracking accuracy under parameter uncertainties and perturbations. Furthermore, due to the adoption of the finite-time exact observer (FEO) for the lumped uncertainty estimation in the controller, the ease of the selection of the control gains is well-achieved since they only depend on the uncertainty estimation error. The closed-loop stability and finite-time convergence are presented based on the Lyapunov stability theory. Experimental verifications are conducted to validate the remarkable performance of the proposed control, in terms of the step and sinusoidal tracking as well as anti-disturbance ability. [ABSTRACT FROM PUBLISHER]

Cascade dynamic nonlinear systems can describe a wide class of engineering problems, but little efforts have been devoted to the identification of such systems so far. One of the difficulties comes from its non-convex characteristic. In this paper, the identification of a general cascade dynamic nonlinear system is rearranged and transformed into a convex problem involving a double-input single-output nonlinear system. In order to limit the estimate error at the frequencies of interest and to overcome the singularity problem incurred in the least-square-based methods, the identification problem is, thereafter, decomposed into a multi-objective optimization problem, in which the objective functions are defined in terms of the spectra of the unbiased error function at the frequencies of interest and are expressed as a first-order polynomial of the model parameters to be identified. The coefficients of the first-order polynomial are derived in an explicit expression involving the system input and the measured noised output. To tackle the convergence performance of the multi-objective optimization problem, the bargaining game theory is used to model the interactions and the competitions among multiple objectives defined at the frequencies of interest. Using the game-theory-based approach, both the global information and the local information are taken into account in the optimization, which leads to an obvious improvement of the convergence performance. Numerical studies demonstrate that the proposed bargaining-game-theory-based algorithm is effective and efficient for the multi-objective optimization problem, and so is the identification of the cascade dynamic nonlinear systems. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed federated Tobit Kalman filter fusion problem for a class of discrete-time systems subject to measurement censoring and packet delays. The censored measurement is characterized by the Tobit measurement model and the packet delay obeys the Poisson distribution. At the input terminal of each local estimator, a censoring detection device and a buffer with finite length are exploited to tackle the censored/delayed measurements. The filtering fusion is carried out in two steps: in the first step, every sensor in the network collects and sends its measurements to the corresponding local estimator via an unreliable network with probabilistic packet delays and, in the second step, the estimates from local estimators are further collected by the fusion center to form a fused estimate. The local estimator runs a modified Tobit Kalman filtering algorithm, which is designed based on the modified Tobit regression model, while the fusion center performs a distributed federated modified Tobit Kalman filtering algorithm, which is devised on the basis of the federated Kalman fusion rule. Given the buffer length, the prescribed bound on the estimation error covariance and the probability distribution of the packet delay, the stability performance of the proposed filtering fusion algorithm is evaluated via a probabilistic approach. Simulation results are provided to show the feasibility of the proposed filter. [ABSTRACT FROM AUTHOR]

This paper introduces a time-domain nondata-aided maximum-likelihood (ML) estimation of Doppler frequency for orthogonal frequency-division multiplexing (OFDM) over doubly selective fading channels with the Clarke–Jakes–Doppler spectrum. The proposed approach adopts the repetitive signal structure of OFDM, such as the cyclic prefix, which can be used for synchronization purpose. The originally derived ML estimation results in a high computational cost; therefore, after gaining insights into the log-likelihood function, we further design an algorithm with reduced complexity. The proposed algorithm only has a complexity $\sim O(QN_{d})$ , where $Q$ and $N_{d}$ denote the numbers of repetitive parts and samples in a repetitive part, respectively. In contrast to conventional works based on (data aided) channel estimates and/or assuming that channels are flat fading, the proposed method employs the cyclostationary feature of received signals and is not sensitive to channel estimation error and unknown channel phases. Furthermore, the Cramér–Rao lower bounds of the Doppler frequency estimate over nonline-of-sight (NLOS) and line-of-sight multipath fading channels are analyzed. Simulations confirm the advantages of the proposed estimator. [ABSTRACT FROM AUTHOR]

Sign information is the key for overcoming the inevitable saturation error in compressive sensing system, which causes loss of information and may result in great bias. For sparse signal recovery from saturation, we propose to use linear loss to improve the effectiveness from the existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, analytical solution in the update progress is obtained and some nonconvex penalties are applicable, e.g., minimax concave penalty, $\ell _0$ norm, and sorted $\ell _1$ norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance can be significantly improved and the computational time is also largely saved, which is verified by the numerical experiments. [ABSTRACT FROM AUTHOR]

Nonsynchronous sampling conditions are the source of leakage errors in measurements based on digital signal processing. The optimal method to avoid such errors is to generate a synchronous sampling clock, whenever the input signal shows enough period stability to allow this. Such method, however, does not yield a straightforward evaluation of the input signal frequency, which is required in several applications. This paper proposes a modified synchronous clock generator that together with a modified frequency interpolation algorithm provides an accurate measurement of the input signal frequency when the only available information about the sampling clock is a given integer multiple of the input signal fundamental frequency. [ABSTRACT FROM AUTHOR]

This paper proposes a rigorous anomaly detection scheme, developed to spot power system operational changes which are inconsistent with the models used by operators. This novel technique relies on a state observer, with guaranteed estimation error convergence, suitable to be implemented in real time, and it has been developed to fully address this important issue in power systems. The proposed method is fitted to the highly nonlinear characteristics of the network, with the states of the nonlinear generator model being estimated by means of a linear time-varying estimation scheme. Given the reliance of the existing dynamic security assessment tools in industry on nominal power system models, the suggested methodology addresses cases when there is deviation from assumed system dynamics, enhancing operators’ awareness of system operation. It is based on a decision scheme relying on analytical computation of thresholds, not involving empirical criteria which are likely to introduce inaccurate outcomes. Since false-alarms are guaranteed to be absent, the proposed technique turns out to be very useful for system monitoring and control. The effectiveness of the anomaly detection algorithm is shown through detailed realistic case studies in two power system models. [ABSTRACT FROM AUTHOR]

This correspondence paper studies the impact of nonidentically distributed channel estimation error on performance of a scheduled space-time block coding with mean-value power allocation in cognitive-radio multiple-input multiple-output system. Using the derived probability density function, the exact closed-form expressions of the proposed system for outage probability, ergodic capacity, and symbol-error rate (SER) with both $\rm M$ -ary quadrature amplitude modulation and phase shift keying modulation are derived. Using asymptotic analysis, this paper quantifies the diversity order for both outage probability and SER in the presence of nonidentically distributed error. From the analysis results, this paper shows that the diversity order is enhanced by the multiuser diversity as the number of user terminals increases regardless of outage probability and SER, while the spatial diversity is eliminated. [ABSTRACT FROM AUTHOR]

In this letter, we discuss cost optimization of sensor networks monitoring structurally full-rank systems under distributed observability constraint. Using structured systems theory, the problem is relaxed into two subproblems: first, sensing cost optimization; and second, networking cost optimization. Both problems are reformulated as combinatorial optimization problems. The sensing cost optimization is shown to have a polynomial-order solution. The networking cost optimization is shown to be NP-hard in general, but has a polynomial-order solution under specific conditions. A 2-approximation polynomial-order relaxation is provided for general networking cost optimization, which is applicable in large-scale system monitoring. [ABSTRACT FROM AUTHOR]

Factors such as machining precision and electrical failures may lead to phase inductance unbalance in a switched reluctance motor (SRM). Under unbalanced inductance conditions, most of the traditional sensorless schemes would be invalid if no modification is applied. To prove the existence of this problem, an inductance partition based rotor position estimation scheme under balanced and unbalanced inductance conditions are investigated and compared for identifying the position estimation error characteristics. The theoretical analysis shows that the position estimation error is highly dependent on the degree of the inductance unbalance. To maintain stable sensorless operation under unbalanced condition, a modified full-cycle inductance partition based rotor position estimation scheme with unbalanced inductance adaptive capability is proposed in this paper. In this method, the linear region of each phase full-cycle inductance is relocated with the predetermined inductance thresholds. By locating the linear inductance regions, the rotor position can be estimated from a simple partition linear model between the rotor position and the phase inductance. This method is easy for implementation without additional hardware and complex calculation. Experiments are performed on a 12/8 structure SRM and the results verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, we study the impacts of cochannel interference (CCI) and channel estimation error (CEE) on the performance of a cellular multiuser two-way relay network. The network comprises of one Na-antenna base station (BS), $M$ single-antenna users, and one L$-antenna relay with N_{r} number of interferers. In particular, by employing antenna selection at the relay, we propose beamforming and antenna selection transmission schemes at the BS with user scheduling in the presence of CEE and CCI. Then, we derive the tight closed-form lower bound expressions for the overall outage probability under Nakagami-m fading. We also deduce the asymptotic outage expressions in the high signal-to-noise ratio regime, from which the impacts of CEE and CCI on the achievable diversity order are highlighted. Moreover, using the asymptotic expressions, three optimization problems to minimize the outage probability are formulated and solved: first, optimal power allocation (OPA) with fixed relay location. Specifically, we develop two OPA approaches, namely, OPA I and OPA II. OPA I allocates the total power appropriately at each terminal, whereas OPA II allocates the fixed half of the total power at the relay and remaining power to base station and users; second, optimal relay location with fixed power allocation; and third, jointly optimal power allocation and relay location. Our results reveal that the OPA I outperforms OPA II, irrespective of different channel/system parameters, interferers, and relay locations. In addition, we demonstrate that the effect of interferers at the relay can be ignored if we allocate sufficient power to the base station and users. It is also shown that the CEE and CCI can severely degrade the outage performance, and even reduce the diversity order to zero. Further, it is highlighted that the impact of CCI can be alleviated by increasing the number of relay antennas. The numerical and simulation results validate our analytical findings, and also demonstrate that the antenna selection scheme offers comparable outage performance as beamforming scheme. [ABSTRACT FROM PUBLISHER]

This paper introduces a new attitude estimation algorithm for pitch and roll angles. Pitch and roll angles are represented by a unit vector, and its estimation error is estimated in the Kalman filter. The main theoretical contribution is that the error covariance equations are simplified to scalar equations. Thus, the proposed algorithm is computationally efficient. The proposed algorithm is also applied to vertical movement estimation. Simulation and experiment results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed fusion estimation problem for time-varying stochastic uncertain systems with multiple asynchronous sampling sensors of different fading measurement rates. Stochastic uncertainties of multiplicative noises exist in the state and measurement equations. The phenomena of fading measurements of different sensors are depicted by a group of stochastic variables with known statistics. Different sensors have different sampling rates. Sampling period of each sensor is uniform and an integer multiple of the state update period. By transforming the multiplicative noises into the additive noises, local estimators (LEs) and estimation error cross-covariance matrices between any two LEs are derived at the state update points. A real-time distributed fusion estimator (DFE) is obtained by using the matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. The asymptotic stability and steady-state property are analyzed. A new conception of $\boldsymbol{{period}}$ steady state is presented and the $\boldsymbol{{period}}$ steady-state properties of the LEs, cross-covariance matrices, and DFE are proven. Though DFE has lower accuracy than the centralized fusion estimator, it has better robustness and flexibility since it has a parallel structure. To avoid the calculation of cross-covariance matrices, a sequential covariance intersection fusion estimator is given by using two-sensor covariance intersection fusion algorithm. It has lower accuracy but smaller computational cost than DFE, and better accuracy than LEs. Two examples are given to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose an open-loop unequal-error-protection querying policy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses. When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the output of a binary symmetric channel, the 20 questions problem can be mapped to an equivalent problem of channel coding with unequal error protection (UEP). A new non-adaptive querying strategy based on UEP superposition coding is introduced, whose estimation error decreases with an exponential rate of convergence that is significantly better than that of the UEP repetition coding introduced by Variani et al. (2015). With the proposed querying strategy, the rate of exponential decrease in the number of queries matches the rate of a closed-loop adaptive scheme, where queries are sequentially designed with the benefit of feedback. Furthermore, the achievable error exponent is significantly better than that of random block codes employing equal error protection. [ABSTRACT FROM AUTHOR]

Novel physical insights are provided into the performances of strictly linear (SL) and widely linear (WL) estimators of the generality of complex-valued data, both proper (second order circular) and improper (second-order noncircular). This is achieved by first performing a novel complementary mean square error (CMSE) analysis, in order to quantify the degrees of improperness (second order noncircularity) of the SL and WL estimation errors. The exact bounds on the CMSE difference between the SL and WL estimators are investigated to show that only a joint consideration of the standard MSE analysis and the proposed CMSE analysis has enough degrees of freedom for a rigorous account of the performance of WL and SL estimators. This also makes it possible to rigourously quantify the contributions to the WL performance advantage from the individual real and imaginary channels, an important finding not possible to obtain by using the standard MSE analysis only. [ABSTRACT FROM AUTHOR]

To correctly insert a gastric tube into the stomach, the position of an attached permanent magnet is estimated by solving the inverse problem using measurement data obtained from magneto-impedance (MI) sensors placed outside the patient’s body. The ubiquity and power of tablet computers make them promising tools to easily confirm the position of the tip of the gastric tube. However, a single mobile device may be insufficient for actually solving the inverse problem. Therefore, we propose a distributed cooperative local access network system for the estimation of the position of a permanent magnet attached to the tip of a gastric tube. In addition, we evaluate whether ferromagnetic materials near a sensor affect the performance of the system. Experimental results show that the position estimation error is less than 10 mm, even when the tip is 150 mm from a MI sensor, and that the tip position can be displayed on a tablet computer every 0.25 s. These results suggest that the proposed method holds promise for hospital applications. [ABSTRACT FROM AUTHOR]

Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in \mathbb R^d with an optimal number of samples. We generalize this problem to the case of spatial signals, where the sampling cost is a function of both the number of samples taken and the distance traveled during estimation. This is motivated by our work studying regions of low oxygen concentration in the Great Lakes. We show that for one-dimensional threshold classifiers, a tradeoff between the number of samples taken and distance traveled can be achieved using a generalization of binary search, which we refer to as quantile search. We characterize both the estimation error after a fixed number of samples and the distance traveled in the noiseless case, as well as the estimation error in the case of noisy measurements. We illustrate our results in both simulations and experiments and show that our method outperforms existing algorithms in a large range of sampling scenarios. [ABSTRACT FROM AUTHOR]

Wide-swath synthetic aperture radar (SAR) missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric SAR (InSAR) time series. However, the processing of the emerging Big Data is challenging for state-of-the-art InSAR analysis techniques. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by the compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. Such interferograms are used to link the “older” data batches with the most recent acquisitions and thus to reconstruct the phase time series. This scheme avoids the necessity of reprocessing the entire data stack at the face of each new acquisition. The proposed estimator introduces negligible degradation compared to the Cramér–Rao lower bound under realistic coherence scenarios. The estimator may therefore be adapted for high-precision near-real-time processing of InSAR and accommodate the conversion of InSAR from an offline to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to state-of-the-art techniques via simulations and application to Sentinel-1 data. [ABSTRACT FROM PUBLISHER]

This paper presents an active disturbance rejection adaptive control scheme via full state feedback for motion control of hydraulic servo systems subjected to both parametric uncertainties and uncertain nonlinearities. The proposed controller is derived by effectively integrating adaptive control with extended state observer via backstepping method. The adaptive law is synthesized to handle parametric uncertainties and the remaining uncertainties are estimated by the extended state observer and then compensated in a feedforward way. The unique features of the proposed controller are that not only the matched uncertainties but also unmatched uncertainties are estimated by constructing two extended state observers, and the parameter adaptation law is driven by both tracking errors and state estimation errors. Since the majority of parametric uncertainties can be reduced by the parameter adaptation, the task of the extended state observer is much alleviated. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically achieves an asymptotic tracking performance in the presence of parametric uncertainties and constant disturbances. In addition, prescribed transient tracking performance and final tracking accuracy can also be guaranteed when existing time-variant uncertain nonlinearities. Comparative experimental results are obtained to verify the high tracking performance nature of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel blind carrier frequency offset (CFO) estimator, namely the sparse recovery assisted CFO (SR-CFO) estimator, for the uplink orthogonal frequency-division multiple access (OFDMA) systems. By exploiting the sparsity embedded in the OFDMA data, the CFO estimation is formulated as an optimization problem of sparse recovery with high-resolution. Meanwhile, in order to enhance the estimation accuracy of CFOs, background noise and sampling errors are mitigated by exploiting the structure of the noise covariances matrix in the transformed observation data, and the asymptotic distribution of the sampling errors. Furthermore, we propose an approach for deriving the regularization parameter used by the SR-CFO estimator, so as to control the tradeoff between the data fitting error and the sparsity of solution. The performance of the proposed SR-CFO estimator along with other four existing estimators is investigated and compared. Numerical results show that the proposed SR-CFO estimator is superior to the state-of-the-art estimators in terms of the estimation reliability. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of velocity estimation in vehicular networks by exploiting the sparsity of vehicle velocity trajectory. First, we exploit the sparsity of vehicular velocity trajectories to reduce the beaconing load on the channel. To this end, we propose a sublayer that reduces the number of transmitted packets through a superframe. At each superframe, each vehicle transmits a few samples of its velocity as well as an encoded measurement of its past velocities. Sparse recovery of each velocity vector is performed at the receiver. We propose the use of repetitions at the medium access control (MAC) layer. Moreover, we extend our scheme into a streaming estimation system without the superframe concept. The streaming system uses a sliding window concept. We thoroughly study the estimation error of the streaming system, and we propose an algorithm to find the best solution over a streaming sliding window. The proposed superframe and streaming schemes are tested with real velocity traces collected in the city of Toronto to capture the performance in the city and highway conditions. Experiment results show that the proposed schemes significantly reduce the number of exchanged packets while preserving the velocity information with an excellent accuracy at the receiver. We also demonstrate how past velocity can be used to enhance vehicle localization accuracy. [ABSTRACT FROM PUBLISHER]

Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results. [ABSTRACT FROM AUTHOR]