Research on Sensor Anti-Saturation Resource Planning Methods under Ballistic Missile Swarm Attacks

To address issues such as insufficient tracking capacity and weak anti-jamming capability of defense systems caused by ballistic missile swarm attacks, poor dynamic adaptability and a lack of multi-band coordination of the traditional networked radar resource management methods, it is necessitated urgently making a breakthrough of the real-time resource optimization in high-density target environments. This paper proposes a joint task-resource optimization framework based on the posterior Cramer-Rao lower bound (PCRLB), constructs a lightweight PCRLB prediction model that reduces computational complexity through Monte Carlo approximation. It designs a two-stage decomposition algorithm to decouple the mixed-integer nonlinear programming problem into discrete radar-target assignment and continuous dwell time allocation phases, also develops a multi-band anti-jamming coordination mechanism to jointly optimize array parameters and frequency offsets. Simulations demonstrate that in a 75-target saturation attack scenario, the tracking position error is reduced by 42.3% compared with traditional methods, and the root mean square error (RMSE) is approaching to the PCRLB theoretical lower bound, and time consumption of the resource allocation algorithm is only 18.7 ms, which meeting millisecond-level real-time requirements, and SNR is increased by 15 dB for the multi-band coordination, while the false alarm rate is reduced by 60%. The proposed framework significantly enhances dense target tracking accuracy and resource utilization efficiency, providing both theoretical and technical support for ballistic missile defense systems with enhanced anti-saturation and anti-jamming capabilities.