Stealth Maneuvering Multi-target Tracking With IMM-LMB Filter
Tao Jiang, Wei Sun, Beining Dai, Jinping Sun · 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · 2021
Aiming at the tracking problem of stealth maneuvering multi-target, a labeled multi-Bernoulli (LMB) filter combined with interacting multiple models (IMM) approach is proposed, which is denoted by IMM-LMB filter. In the prediction step of IMM-LMB filter, the mixed prediction of multi-target state is executed. Then, the Gibbs sampling method is used to get the result of measurement partition. Finally, the posterior probability density of multi-target and the model probability are updated interactively by using the quasi-partition measurement subsets. Moreover, the Gaussian mixture implementation of IMM-LMB filter is given. Simulation results show that the proposed IMM-LMB filter is able to track stealth maneuvering multi-target stably. Compared with the LMB filter, the OSPA distance of the proposed filter is smaller and the cardinality estimation is more current.