Stochastic Outlier Selection via GM-CPHD Fusion for Multitarget Tracking Using Sensors With Different Fields of View
Liu Wang, Guifen Chen, Lei Zhang, Tong Wang · IEEE Sensors Journal · 2024
In response to the issue of different perspectives affecting fusion accuracy in distributed multi-sensor multi-target tracking (DMMT), this study investigates the fusion strategies for multi-target tracking with different field-of-view sensors. We explore the impacts of different fields of view (FoVs) on the multi-sensor Geometric Average/Arithmetic Average (GA/AA), generalized covariance intersection(GCI)fusion strategies, and differential perspective boundary segmentation using examples. A Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) fusion method for selecting random outliers in multi-target tracking based on limited perspective sensors is proposed. The boundary is segmented according to different perspectives, and the posterior intensity function is decomposed into multiple sub-intensities using Stochastic Outlier Selection(SOS) clustering. The distribution of the number of targets in the corresponding region is characterized using a multi Bernoulli reconstruction cardinal distribution. The complexity of the validation algorithm is sim-ilar to that of CPHD filtering. A simulation verifies the robustness and effectiveness of this method. By calculating OSPA(Optimal Sub-Pattern Assignment) and algorithm runtime, it can be verified that:the algorithm is more robust based on the reduction in detection probability and the increase in the clutter rate.