Interacting multiple sensor filter

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 Cramér–Rao lower bound (PCRLB) is derived for multisensor collaborative tracking. Finally, simulation results show the effectiveness of the proposed IMSF algorithm.

► We design the relaying Kalman filter by reconstructing the innovation equation.
► We propose the interacting multiple sensor filter adapted to nonlinear cases.
► We drive the PCRLB for multisensor collaborative tracking.