Distributed self-organized cluster-based fusion tree generation algorithm

Kyuoke Yeun, Tae Joon Jun, Daeyoung Kim

Abstract: A fusion system, which collects air-tracks from distributed radars and eliminates duplicated tracks, is required to get a Single Integrated Air Picture (SIAP) for the wide surveillance area. We are developing a distributed radar system which consists of numerous mobile radars to cover wide surveillance area. Two-tier fusion system, which is the well-known solution for wide surveillance area, was adopted. Two-tier fusion system allows to put local fusion nodes between local radars and the central fusion node. We argue that the number of processed tracks of the two-tier fusion system is highly correlated with the fusion tree which decides the local fusion nodes and their child radars. We improved the number of processed tracks of our radar system by applying the fusion tree control which has been neglected in the most of research in this field. However it is hard to generate a proper fusion tree due to the possible number of fusion tree increases exponentially as the number of radars increase. To solve this problem, we propose a novel self-organized fusion tree generation algorithm. Especially, the proposed solution generates the fusion tree without any prior information, such as network-topology, and position of radars. We evaluate the performance of the proposed solution using the OPNET network simulator and show that the proposed solution performs better than the naive methods.