Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

Abstract

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