Dynamic Sensor Management for Multisensor Multitarget Tracking

We study the problem of sensor scheduling for multisensor multitarget tracking-to determine which sensors to activate over time to trade off tracking error with sensor usage costs. Formulating this problem as a partially observable Markov decision process (POMDP) gives rise to a non-myopic sensor-scheduling scheme. Our method combines sequential multisensor joint probabilistic data association (MS-JPDA) and particle filtering for belief-state estimation, and uses simulation-based Q-value approximation method for "lookahead". The example of focus in this paper involves the activation of multiple sensors simultaneously for tracking multiple targets, illustrating the effectiveness of our approach.

[1]  Lucy Y. Pao,et al.  A comparison of parallel and sequential implementations of a multisensor multitarget tracking algorithm , 1995, Proceedings of 1995 American Control Conference - ACC'95.

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

[3]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[4]  Ying He,et al.  Sensor scheduling for target tracking in sensor networks , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[5]  Vikram Krishnamurthy,et al.  Algorithms for optimal scheduling and management of hidden Markov model sensors , 2002, IEEE Trans. Signal Process..

[6]  Fredrik Gustafsson,et al.  Monte Carlo data association for multiple target tracking , 2001 .

[7]  Edwin K. P. Chong,et al.  Sensor scheduling for target tracking: A Monte Carlo sampling approach , 2006, Digit. Signal Process..

[8]  Lucy Y. Pao,et al.  Multisensor Fusion Algorithms for Tracking , 1993 .

[9]  Sebastian Thrun,et al.  Particle Filters in Robotics , 2002, UAI.

[10]  Robert Givan,et al.  Parallel Rollout for Online Solution of Partially Observable Markov Decision Processes , 2004, Discret. Event Dyn. Syst..

[11]  Alfred O. Hero,et al.  Multi-target Sensor Management Using Alpha-Divergence Measures , 2003, IPSN.

[12]  Vikram Krishnamurthy,et al.  Optimal sensor scheduling for Hidden Markov models , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[13]  Alf Isaksson,et al.  On sensor scheduling via information theoretic criteria , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[14]  Y. Oshman.  Optimal sensor selection strategy for discrete-time state estimators , 1994 .

[15]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[16]  D. Castañón.  Approximate dynamic programming for sensor management , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[17]  Dieter Fox,et al.  Real-time particle filters , 2004, Proceedings of the IEEE.

[18]  Bo Wahlberg,et al.  Finite dimensional algorithms for optimal scheduling of hidden Markov model sensors , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[19]  Dimitri P. Bertsekas,et al.  Rollout Algorithms for Stochastic Scheduling Problems , 1999, J. Heuristics.

[20]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[21]  O. Hernondex-lerma,et al.  Adaptive Markov Control Processes , 1989 .

[22]  K. Kastella,et al.  Particle filtering for multitarget detection and tracking , 2005, 2005 IEEE Aerospace Conference.

[23]  Lucy Y. Pao,et al.  Multisensor Fusion Algorithms for Tracking , 1993, 1993 American Control Conference.