Anti-Deception Jamming Resource Scheduling for Multitarget Tracking in Distributed Radar Networks

In this article, we propose an anti-deception jamming joint radar-to-target assignment and power scheduling (ADJ-JRAPS) strategy in distributed radar networks to discriminate the false target and achieve multitarget tracking (MTT) under deception jamming. Specifically, the deception parameters, which play significant roles in distinguishing the physical targets from false targets, are integrated with the target state to be estimated in MTT. Based on target detection conditions under deception jamming, the posterior Cramér–Rao lower bound (PCRLB) of the augmented state is derived. Then, a discriminator, based on the derived PCRLB, is designed to calculate the false target rejection probability, which quantifies the discrimination performance of the radar system. Combined with system resource constraints, an ADJ-JRAPS problem for target discrimination and MTT under deception jamming is constructed. Due to the coupling of variables and the nonconvexity of the false target rejection probability, the formulated problem exhibits nonconvexity and is NP-hard. Hence, we first decompose the coupled variables and introduce a two-stage continuous convex approximate method to address this problem efficiently. Simulation experiments indicate that the presented ADJ-JRAPS has outstanding capabilities in handling false target discrimination and MTT problems under deception jamming.