Multi-model particle cardinality-balanced multi-target multi-Bernoulli algorithm for multiple manoeuvring target tracking

Multiple manoeuvring target tracking is an extremely difficult problem in the target tracking field, especially under the non-linear systems. The probability hypothesis density (PHD) and cardinalised PHD (CPHD) algorithms based on the particle filter have proved to be promising algorithms for multi-target tracking. However, they have a heavy computational burden because of the particle clustering in the stage of state extraction. Especially, the additional calculation is added to the CPHD algorithm because of the estimation of the cardinality distribution. To solve the problem, the authors propose a novel multiple manoeuvring target tracking algorithm by extending the multi-model method to the cardinality-balanced multi-target multi- Bernoulli filter and then using the sequential Monte Carlo implementation. Moreover, in order to obtain the individual target tracks, the particle labelling technique is introduced in the proposed algorithm. Simulation results show that the proposed algorithm can effectively achieve the track continuity for the multiple manoeuvring target tracking and has a higher accuracy of state estimates than the multiple model particle PHD and CPHD algorithm with a better computational efficiency.