Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

This paper studies the multitarget tracking problem based on an asynchronous network of sensors with different sampling rates, where each sensor runs a cardinalized probability hypothesis density (CPHD) filter. To fuse filter estimates obtained at conditioned measurements, arithmetic averaging approach is recursively carried out in timely manner according to network-wide time sequence. The intersensor communication conducted by so-called partial flooding scheme, which either cardinality distributions or intensity functions pertinent local posteriors are disseminated among sensors. fused results may not feedback filter, will avoid delay filters cased fusion expense reduced information gain. Furthermore, extension proposed multi-sensor CPHD bootstrap filtering algorithm given accommodate unknown clutter rate and detection profile. Numerical simulations performed test approaches.