Distributed Multi-Target Tracking with D-DBSCAN Clustering

Abstract

This paper proposes a novel clustering-based distributed multi-target tracking algorithm over a sensor network. Each local sensor runs a joint probabilistic data association filter to obtain local state estimation. The estimates are communicated between connected sensors for track-totrack association and fusion. A novel distributed DBSCAN (D-DBSCAN) clustering algorithm is proposed to solve the track-to-track association problem. The proposed algorithm shows advantages in computational efficiency compared with conventional distributed multi-target tracking approaches. Extensive simulations provided substantial evidence for the effectiveness of the proposed algorithm.