Interacting multiple sensor filter

Authors:
Zhigang Liu;Jinkuan Wang;Yanbo Xue
Affiliations:
Insitute of Engineering Optimization and Smart Antenna, Northeastern University, Qinhuangdao 066004, China;Insitute of Engineering Optimization and Smart Antenna, Northeastern University, Qinhuangdao 066004, China;Cognitive Systems Laboratory, McMaster University, Hamilton, Ontario, Canada L8S 4K1
Venue:
Signal Processing
Year:
2012

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Abstract

Due to the limited sensing range of sensors, moving target tracking has to be realized by relaying from one sensor to the other in wireless sensor networks. Therefore, the tracking procedure can be modelled as a Markov chain system. By reconstructing the innovation equation, the relaying Kalman filter (RKF) algorithm is designed in light of Bayesian theory. To deal with nonlinear cases, the interacting multiple sensor filter (IMSF) is proposed in this paper by using the unscented Kalman filter (UKF), the extended Kalman filter (EKF) or the particle filter (PF). Then, the posterior Cramer-Rao lower bound (PCRLB) is derived for multisensor collaborative tracking. Finally, simulation results show the effectiveness of the proposed IMSF algorithm.