Distributed Cross-Entropy $\delta$ -GLMB Filter for Multi-Sensor Multi-Target Tracking

Abstract

The multi-dimensional assignment problem, and by extension the problem of finding the $T$-best (i.e., the $T$ most likely) multi-sensor assignments, represent the main challenges of centralized and especially distributed multi-sensor tracking. In this paper, we propose a distributed multi-target tracking filter based on the $\delta$ -Generalized Labeled Multi-Bernoulli (6-GLMB) family of labeled random finite set densities. Consensus is reached for high-scoring multi-sensor assignments jointly across the network by employing the cross-entropy method in conjunction with average consensus. This ensures that multi-sensor information is jointly used to select high-scoring multi-assignments without exchanging the measurements across the network and without exploring all possible single-target multi-assignments. In contrast, tracking algorithms that rely on posterior fusion, i.e., merging local posteriors of neighboring nodes until convergence, are suboptimal due to the use of only local information to select the $T$-best local assignments in the construction of local posteriors. Numerical simulations showcase this performance improvement of the proposed method with respect to a posterior-fusion δ- GLMB filter.