Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks

Maşazade, Engin; Fardad, Makan; Varshney, Pramod K.

doi:10.1109/lsp.2012.2220350

Cited by 79 publications

(76 citation statements)

References 13 publications

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“…We also remark that although the dynamical system (36)-(37) is assumed to be linear, it will be evident later that the proposed sensor scheduling framework is also applicable to non-linear dynamical systems. The PDF of the initial state x 0 at time step t 0 is assumed to be Gaussian with meanx 0 and covariance matrixP 0 , wherex 0 andP 0 are estimates of the initial state and error covariance from the previous measurements obtained using filtering algorithms, such as a particle filter or a Kalman filter [37], [38]. At time step t 0 , we aim to find the optimal sensor schedule over the next τ time steps t 0 + 1, t 0 + 2, .…”

Section: Non-myopic Sensor Schedulingmentioning

confidence: 99%

“…In this equation, the expectation with respect to x t is commonly calculated with the help of the prediction statex t := F t−1 F t−2 · · · F 0x0 [38], [40]. To be concrete, we approximate the PDF of x t with p(x t ) = δ(x t −x t ), where δ(·) is a δ-function.…”

Section: Non-myopic Sensor Schedulingmentioning

confidence: 99%

“…T , where (x t,1 , x t,2 ) and (x t,3 , x t,4 ) denote the target location and velocity at time step t. The state equation (36) follows a white noise acceleration model [38] …”

Section: Sensor Scheduling For State Trackingmentioning

confidence: 99%

See 2 more Smart Citations

Sensor Selection for Estimation with Correlated Measurement Noise

Liu

Chepuri

Fardad

et al. 2016

IEEE Trans. Signal Process.

167

107

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Abstract-In this paper, we consider the problem of sensor selection for parameter estimation with correlated measurement noise. We seek optimal sensor activations by formulating an optimization problem, in which the estimation error, given by the trace of the inverse of the Bayesian Fisher information matrix, is minimized subject to energy constraints. Fisher information has been widely used as an effective sensor selection criterion. However, existing information-based sensor selection methods are limited to the case of uncorrelated noise or weakly correlated noise due to the use of approximate metrics. By contrast, here we derive the closed form of the Fisher information matrix with respect to sensor selection variables that is valid for any arbitrary noise correlation regime, and develop both a convex relaxation approach and a greedy algorithm to find near-optimal solutions. We further extend our framework of sensor selection to solve the problem of sensor scheduling, where a greedy algorithm is proposed to determine non-myopic (multi-time step ahead) sensor schedules. Lastly, numerical results are provided to illustrate the effectiveness of our approach, and to reveal the effect of noise correlation on estimation performance.

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Section: Non-myopic Sensor Schedulingmentioning

confidence: 99%

Section: Non-myopic Sensor Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

Sensor Selection for Estimation with Correlated Measurement Noise

Liu

Chepuri

Fardad

et al. 2016

IEEE Trans. Signal Process.

167

107

View full text Add to dashboard Cite

show abstract

“…The past estimate and prediction can be computed from an extended Kalman filter (EKF) for instance [18]. One of the functions related to A-, E-, or D-optimality of the error covariance matrix computed using the past state estimate can then be used as a performance measure to perform selection [20]. However, since the past state estimate (not the true state) is used to compute the covariance matrix, depending on the non-linearity of the model and the noise variance, the selection will be suboptimal.…”

Section: B Sensor Selection For Filteringmentioning

confidence: 99%

Sensor selection for estimation, filtering, and detection

Chepuri

Leus

2014

2014 International Conference on Signal Processing and Communications (SPCOM)

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Abstract-Sensor selection is a crucial aspect in sensor network design. Due to the limitations on the hardware costs, availability of storage or physical space, and to minimize the processing and communication burden, the limited number of available sensors has to be smartly deployed. The node deployment should be such that a certain performance is ensured. Optimizing the sensors' spatial constellation or their temporal sampling patterns can be casted as a sensor selection problem. Sensor selection is essentially a combinatorial problem involving a performance evaluation over all possible choices, and it is intractable even for problems of modest scale. Nevertheless, using convex relaxation techniques, the sensor selection problem can be solved efficiently. In this paper, we present a brief overview and recent advances on the sensor selection problem from a statistical signal processing perspective. In particular, we focus on some of the important statistical inference problems like estimation, tracking, and detection.

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“…Most of the papers assume that the signal propagation is line of sight (LOS) between the MN and the beacon nodes (BN). We can obtain an accurate distance estimation by using the filtering techniques [7]. However, the direct propagation path between the MN and the BN may be blocked by obstacles in practical applications of WSNs, such as the indoor and urban environments.…”

Section: Introductionmentioning

confidence: 99%

Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

Liu

et al. 2015

Telecommun Syst

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As one of the key technologies of wireless sensor networks (WSNs), the localization of mobile nodes (MN) is one of the most significant research topics in WSNs. When a line-of-sight (LOS) channel is available, accuracy localization result can be obtained. Motivated by the fact that the non-line-of-sight (NLOS) propagation of signal is ubiquitous and decreases the accuracy of localization, we propose a MN localization algorithm in mixed LOS/NLOS environments. Considering the characteristics of NLOS error, we propose a localization algorithm based on vote selection mechanisms to filter the distance measurements and reserve the reliable measurements. Then a modified probabilistic data association algorithm is proposed to fuse the multiple measurements reserved from the vote selection. The position of the MN is finally determined by a linear least squares algorithm based on reference nodes selection. This algorithm effectively mitigates various kinds of NLOS errors and largely improves the localization accuracy of the MN in mixed LOS/NLOS environments. The simulation and experiments results show that the proposed algorithm has better robustness and higher localization accuracy than other methods. Electronic supplementary materialThe online version of this article

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Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks

Cited by 79 publications

References 13 publications

Sensor Selection for Estimation with Correlated Measurement Noise

Sensor Selection for Estimation with Correlated Measurement Noise

Sensor selection for estimation, filtering, and detection

Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

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