Abstract:

To solve the measurement innovation weakening problem in the cardinality balanced multi-target multi-Bernoulli(CBMeMBer) filter, an improved multi-target multiBernoulli (MeMBer) filter is proposed by modifying the legacy rather than the measurement updated tracks parameters. Then, to provide a closedform solution to the nonlinear problem occurred in the passive bearings only multi-target tracking system, a set of Gaussian particles is employed to approximate the distributions of the multi-Bernoulli random finite set. The simulation results show that the proposed algorithm can get nearly the same tracking accuracy as that of the Gaussian mixture particlecardinalized probability hypothesis density filter with the much lower computational cost, implying good application prospects.