| Summary: | The sequential Monte Carlo cardinality balanced multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with the multi-target tracking problem. However, the standard SMC-CBMeMBer filter suffers from the particles’ degradation problem seriously. Using the measurements to construct the proposal density in the step of predict can effectively solve the above problem, but this kind of approach brings an amount of computation and causes the overestimation of the target number. To examine the quality of each predicted particle adaptively and use the cubature Kalman filter (CKF) to refine the poor-quality particles with the aid of the current measurements is proposed in this study. This method manages to alleviate the particles degradation problem without increasing the computational complexity seriously since only a part of the particles is refined by the CKF. Also, the proposed method can avoid cardinality overestimation caused by abuse of measurements. A range of simulations is performed to test the performance of the proposed method. The results confirm the effectiveness and robustness of the novel method.
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