Beam Pattern-Aware Resource Allocation for Phased Array Radar Network Under Suppression Jamming

In this article, a beam pattern-aware beam and dwell time allocation strategy (BPBTAS) is proposed for multitarget tracking (MTT) in the phased array radar network (PARN) under suppression jamming. The PARN adopts a distributed architecture, where the local radar with the successful detection state sends the updated statistics of the target state to the fusion center for joint calculation of global MTT results. First, we establish the signal model of the PARN under suppression jamming and analytically discuss the effect of the beam pattern on the signal model through the beam gain models of the radar and the jammer. Considering the degraded target detection conditions, the posterior Cramér-Rao lower bound (PCRLB) scaled by an information reduction factor (IRF) is derived and regarded as a performance metric for target tracking under suppression jamming. Then, the mapping function of radar resources, beam pattern, and tracking performance is constructed and used as the optimization objective. Finally, due to the coupling between resource variables and the nonconvexity of IRF-based PCRLB, a contribution-based four-stage traversal iterative algorithm is proposed for the mixed-integer nonconvex problem. Simulations validate the superiority of the proposed BPBTAS for tracking performance under different suppression jamming scenarios.