An improved algorithm of distributed multi-sensor multi-target tracking

Distributed multi-sensor (DMS) network can effectively increase the coverage of the sensors and improve the ability of detection and tracking for moving targets. However, the Generalized Covariance Intersection (GCI) based fusion algorithm is suffer from the problem that the tracking performance will be deteriorated under complex environment. In this paper, we proposed an improved distributed fusion algorithm under the multi-Bernoulli (MB) filter framework for improving the tracking performance under complex environment. First, a decision-level fusion strategy is proposed to extract more accurate estimation states, and then a feature-level fusion feedback strategy is proposed to reduce the negative influences of the inaccurate fusion results for the subsequent filtering process. Moreover, an interactive feedback strategy is proposed to avoid the miss tracking of each single sensor. The experimental results show that the proposed algorithm has a better tracking accuracy than the traditional GCI-based distributed fusion algorithm and the traditional particle filter MB (PF-MB) tracking algorithm, with a good multi-target tracking ability in complex environments.