Centralized Information Fusion with Limited Multi-View for Multi-Object Tracking

In practical radar detection applications, due to the limitation of the beam width of the pattern, limited field of view (FOV) lacks the overall perception ability of the area of interest (AOI). Especially, when unknown and time-varying targets appear in AOI, it can easily lead to missing even wrong tracking of key objects. In view of the above problems, the radar network is adopted to fuse the observation data of limited multi-view to obtain the global field of view information, and then realize the trajectories estimation of multi-object in the fusion center. Based on FInite Set STatistics (FISST) framework, mapping the newborn and death process of multiple targets within FOVs as multi-Bernoulli process, the posteriori density of multi-objects is propagated recursively followed Bayesian criterion in time. The simulation results of multi-object trajectories estimation with four kinds of multi-Bernoulli (MB) filters are given under three scenarios, which illustrates that the number of interest objects and the accuracy of trajectories estimation are improved, along with the increase of the number of local observation fields of view. Furthermore, the tracking performance of labeled multi-Bernoulli (LMB) filter is superior to that of unlabeled filter.