Multiple-model GM-CBMeMBer filter and track continuity

A multi-model cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper for tracking multiple maneuvering targets and forming the multi-target trajectories in clutter. Given the assumptions that the dynamic and observation models of the multi maneuvering targets are linear-Gaussian and by applying the Gaussian mixture (GM) technique, the analytic recursion for the proposed filter, namely the multi-model GM-CBMeMBer filter, is obtained. The extended Kalman (EK) filtering approximations for the multi-model GM-CBMeMBer filter to accommodate non-linear models are described briefly. Simulation results show that the proposed filter performs multiple maneuvering targets tracking well whereas the single-model GM-CBMeMBer filter obviously produces the missing and false trajectories. In addition, simulation results also show that for the scenarios of the relatively low signal-to-noise ratio (SNR), the performance of the proposed filter is better than that of the multi-model GM probability hypothesis density (GM-PHD) filter, and is close to that of the multi-model GM cardinalized PHD (GM-CPHD) filter.