Low probability of intercept-based resource allocation algorithm for distributed phased array radar network

This paper presents a low probability of intercept (LPI)-based joint node selection and resource allocation (LPI-JSRA) strategy for multiple target tracking (MTT), aimed at enhancing both tracking accuracy and LPI performance in a distributed phased array radar (PAR) network. First, taking into account the impact of radar node selection, transmit power, and dwell time on LPI performance, a cooperative optimization strategy is designed to minimize the system’s intercept probability while maintaining required tracking accuracy. To achieve this, a closed-form expression for the posterior Cramér-Rao lower bound (PCRLB) with controllable parameters is derived, which serves to quantify MTT performance and act as an optimization constraint. Furthermore, federated extended Kalman filter (FEKF) is used to conduct target tracking estimation within the radar network, improving the system’s ability to perform cooperative tracking. To address the resulting mixed-integer non-convex optimization problem, a two-step solution method based on polar lights optimization (PLO) is proposed. This approach efficiently optimizes radar resource allocation, ensuring the optimal configuration of system resources. Finally, extensive numerical simulations confirm the effectiveness of the proposed LPI-JSRA strategy in distributed PAR network and highlight its significant advantages in improving LPI performance.