Centralized multi-sensor labeled multi-Bernoulli filter with partially overlapping fields of view.

In this paper, the labeled multi-Bernoulli (LMB) filter is extended to the centralized multi-sensor (MS) case, resulting in a multi-sensor multi-object tracking (MS-MOT) algorithm having the ability to cope with partially overlapping fields-of-view (FoVs). Specifically, a joint prediction and update scheme for the MS-LMB filter is presented and the inherent NP-hard multi-dimensional assignment problem is solved via Gibbs sampling. We propose a suboptimal stationary distribution for the Gibbs sampling which admits conditionally independent sampling among multiple sensors thus dramatically decreasing the computational complexity. In addition, in order to tune object initialization when there is no background knowledge of the birth locations, a novel MS measurement-driven birth process is proposed. The proposed birth process has superior ability in terms of suppressing false tracks originating from clutter measurements of multiple sensors. The effectiveness of the proposed algorithm and birth process is demonstrated in simulation experiments concerning MS surveillance scenarios with partially overlapping sensor FoVs.