Performance comparison of several nonlinear multi-Bernoulli filters for multi-target filtering

In this paper, the performance of four nonlinear multi-Bernoulli (MB) filters for multi-target filtering is compared in the presence of clutter and detection uncertainty. The filters under consideration are the extended Kalman (EK) Gaussian mixture (GM) MB filter, the unscented Kalman (UK) GM-MB filter, the cubature Kalman (CK) GM-MB filter, and the sequential Monte Carlo (SMC) MB filter. Monte Carlo (MC) analyses are presented for these four filters under different clutter density and different detection probability. Then these filters are evaluated in terms of both the Optimal Sub-Pattern Assignment (OSPA) distance and their respective computing time. Simulation results show that the CK-GM-MB filter is an attractive nonlinear MB filtering approach.