An approximate dynamic programming based non-myopic sensor selection method for target tracking

In this paper, we study the non-myopic sensor selection problem for target tracking in wireless sensor networks based on quantized sensor data. Using the conditional posterior Cramér-Rao lower bound (C-PCRLB) as a sensor selection metric, we formulate and solve a non-myopic sensor selection problem using an approximate dynamic programming (A-DP) algorithm. Given a constraint on the total number of selected sensors allowed while observing the target over a time window, simulation results show that the proposed non-myopic sensor selection scheme based on A-DP is computationally very efficient and yields better tracking performance than the myopic sensor selection scheme.

[1]  John W. Fisher,et al.  Approximate Dynamic Programming for Communication-Constrained Sensor Network Management , 2007, IEEE Transactions on Signal Processing.

[2]  Pramod K. Varshney,et al.  A sensor selection approach for target tracking in sensor networks with quantized measurements , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[4]  Alfred O. Hero,et al.  Sensor management using an active sensing approach , 2005, Signal Process..

[5]  P.K. Varshney,et al.  Target Location Estimation in Sensor Networks With Quantized Data , 2006, IEEE Transactions on Signal Processing.

[6]  Y. Bar-Shalom,et al.  Multisensor resource deployment using posterior Cramer-Rao bounds , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Pramod K. Varshney,et al.  Conditional Posterior Cramér–Rao Lower Bounds for Nonlinear Sequential Bayesian Estimation , 2012, IEEE Transactions on Signal Processing.

[8]  Pramod K. Varshney,et al.  Energy Aware Iterative Source Localization for Wireless Sensor Networks , 2010, IEEE Transactions on Signal Processing.

[9]  K. Punithakumar,et al.  Multisensor deployment using PCRLBS, incorporating sensor deployment and motion uncertainties , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Pramod K. Varshney,et al.  Posterior Crlb Based Sensor Selection for Target Tracking in Sensor Networks , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[11]  Stephen P. Boyd,et al.  Sensor Selection via Convex Optimization , 2009, IEEE Transactions on Signal Processing.

[12]  Carlos H. Muravchik,et al.  Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..

[13]  Feng Zhao,et al.  Information-driven dynamic sensor collaboration , 2002, IEEE Signal Process. Mag..

[14]  Pramod K. Varshney,et al.  Dynamic Bit Allocation for Object Tracking in Wireless Sensor Networks , 2011, IEEE Transactions on Signal Processing.

[15]  Pramod K. Varshney,et al.  Conditional Posterior Cramér-Rao Lower Bounds for Nonlinear Sequential Bayesian Estimation , 2011, IEEE Trans. Signal Process..

[16]  Claire J. Tomlin,et al.  Mobile Sensor Network Control Using Mutual Information Methods and Particle Filters , 2010, IEEE Transactions on Automatic Control.

[17]  Pramod K. Varshney,et al.  Dynamic Bit Allocation for Object Tracking in Bandwidth Limited Sensor Networks , 2011, arXiv.org.