Distributed multi-view multi-target tracking based on CPHD filtering

This paper addresses distributed multi-target tracking over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinalized probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has limited FoV, the standard fusion methods need to be suitably modified. In fact, the monitored area of multiple sensor nodes consists of several parts that are either exclusive of a single node, i.e. or common to multiple (at least two) nodes. In this setting, the crucial issue is how to account for these different information sets in the fusion rule. The problem is particularly challenging when the knowledge of the FoVs is unreliable, for example because of the presence of unknown occlusions, and when local sensors can have valuable information also on targets located outside the current nominal FoV, for example thanks to the diffusion of information in the network or because the FoVs are time-varying. In this context, we propose a distributed CPHD filter based on the idea of decomposing the posterior densities to be fused so as to account for the different information sets. A new decomposition method is proposed that does not rely on the nominal FoVs and, instead, uses clustering to decompose the posterior intensity function into multiple sub-intensities, and reconstructing the corresponding cardinality distribution via multi-Bernoulli approximation. Then, fusion is performed in parallel according to either the geometric average or arithmetic average rule. Simulation experiments are provided to demonstrate the effectiveness of the proposed approach.