Radar assignment for stealth targets detection and tracking based on BPSO in air-defense radar network

In this paper we consider the radar assignment problem for air-defense radar network to detect and track multiple stealth targets. For the purpose of selecting appropriate radar at appropriate time to optimize the detection and tracking performance under the given constraints, we propose a novel radar assignment algorithm based on posterior Cramér-Rao lower bound (PCRLB), binary particle swarm optimization (BPSO) and particle filtering (PF). A group of randomly generated particles are used to obtain the detection probability of newborn targets, while the tracking accuracy is measured by PCRLB. Moreover, the BPSO is adopted to search the surveillance region for the optimal activated radar subset. Finally, the PF outputs of activated radars are fused. Simulation results show that the proposed method can not only quickly identify newborn targets, but also can optimize the tracking performance of existing targets. Compared with traditional methods, the tracking accuracy of the radar network is improved.