Distributed Multi-Sensor Cphd Filter Using Pairwise Gossiping

We present a distributed cardinalized probability hypothesis density (CPHD) filter for multi-sensor multi-target tracking. Each sensor runs a single-sensor CPHD filter to compute the probability hypothesis density (PHD) function and cardinality distribution using only its own measurements and then fuses the local results by gossiping with neighboring sensors. Existing schemes that fuse local results using the Kullback-Leibler average are adversely affected if some sensors do not detect a target. The proposed fusion strategy, based on the arithmetic mean instead of the geometric mean, aims to be more robust to missed detections. We also show via simulations that the performance of the proposed algorithm can be significantly improved, with a small additional communication overhead, by having sensors exchange measurements locally.