Joint Selection and Power Allocation Strategy for Target Tracking in Decentralized Multiple Radar Systems

Multiple radar systems have shown significant advances in target tracking. Reasonable power allocation strategy can sufficiently utilize the limited power resources and in turn, improve the system tracking performance. However, towards the existing power allocation strategies, the system configuration is only restricted to the centralized architecture, and practical communication requirements and system robustness have not been considered. To tackle these problems, we propose a joint selection and power allocation (JSPA) strategy for target tracking in multiple radar systems with decentralized configuration. The optimal fusion is presented to obtain the global posterior distribution in terms of the local filtering densities. Then the corresponding weights for the fusion estimation can be updated according to distributed particle filter (DPF). Finally, the decentralized posterior Cramer-Rao lower bound (PCRLB) is derived based on the optimal fusion, and consequently, employed as an optimization metric for JSPA strategy. Numerical results demonstrate the superior performance of the proposed strategy.