Abstract
A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.
