An Adaptive Threshold Reward Function for Radar Anti-Jamming Decision-Making

Abstract

This study proposes an adaptive reward function to improve the convergence speed and adaptability of radar intelligent anti-jamming models.The design is based on two key factors: interference suppression effectiveness and target integrity after suppression. The primary reward is the improvement in signal-to-interference-plus-noise ratio (SINR), a standard metric for anti-jamming performance. To better distinguish between strategies, three performance indicators—interference suppression ratio (ISR), target amplitude fidelity (TAF), and target detection integrity (TDI)—are used as threshold constraints. An adaptive threshold mechanism reduces outlier rewards, accelerating convergence and improving flexibility and robustness across interference environments. Experiments show that the proposed method converges faster than existing approaches: the simulation results show a 40%–45% reduction in convergence time, and the anechoic chamber tests show a 60% reduction. The method also performs well under main lobe, side lobe, and suppression interference scenarios.