Abstract:

For the problem that the standard labeled multi-Bernoulli (LMB) filter only considers the single motion model case, a multiple model LMB (MM-LMB) filter for maneuvering target tracking is proposed. By introducing the jump Markov (JM) system to the LMB method, the extended recursion formulations are presented, and the sequential Monte Carlo implementation of the proposed method is given. Simulations show that the MM-LMB filter can track multiple maneuvering targets effectively, and has higher tracking accuracy than the multiple model probability hypothesis density (MM-PHD) filter and the multiple model cardinality balanced multitarget multiBernoulli (MM-CBMeMBer) filter in complex detection environment. The calculation cost of the proposed method is lower than MM-PHD and MM-CBMeMBer when the targets are not closed, while grows faster than the compared algorithms when the targets gather together.