Neighbor Dependency-Based Dynamic Fusion Tree Generation for a Multi-radar System

Abstract

This paper proposes a fusion tree generation (FTG) algorithm for a two-tier fusion process in a multi-radar system. The two-tier fusion process divides the fusion process into local and global parts. The fusion workload of a multi-radar system at the central server can be reduced by applying two-tier approach. However, the two-tier fusion process requires a balanced fusion tree to increase the number of processed tracks. This paper presents a dynamic fusion tree generation (D-FTG) algorithm based on a clustering scheme. We developed a novel clustering scheme, which can be used in generating balanced fusion trees in a multi-radar system. The developed clustering scheme is able to generate a balanced fusion tree with the neighbor dependency-based scoring method, manage the dynamic changes of the distribution of targets with the pruning-and-rejoining strategy, and cope with the failure of radar nodes by repeating initial clustering. Simulation results show that D-FTG outperforms existing clustering methods when used to generate balanced fusion trees in a dynamic multi-radar system.