Xu, Shuoyuan (Cranfield University), Shin, Hyo-Sang (Cranfield University), Tsourdos, Antonios (Cranfield University)

Distributed Multi-Target Tracking with Distributed DBSCAN Clustering

Scheduled for presentation during the Regular Session "Sensor Fusion" (MoD15T2), Monday, November 25, 2019, 16:00−16:20, Room T2

2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS), November 25-27, 2019, Cranfield University, Cranfield, UK

This information is tentative and subject to change. Compiled on April 25, 2024

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-to-track 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.