Abstract
Range gate pull-off (RGPO) jamming is an effective method for track deception aimed at radar systems. Nevertheless, enhancing the effectiveness of the jamming strategy continues to pose challenges, restricting the RGPO jamming method from achieving its maximum potential. This paper focuses on addressing the problem of optimizing the strategy for white-box RGPO jamming, serving as a foundational step toward quantitative optimization research on RGPO jamming strategies. In the white-box scenario, it is presumed that the jammer has full knowledge of the target radar's tracking system, encompassing both the choice of tracking method and its parameter configurations. The intricate interactions between the jammer and the tracking radar introduce three primary challenges: (1) Formulating an algebraic expression for the objective function of the jamming strategy optimization is nontrivial; (2) Direct observation of jamming effects from the target radar is challenging; (3) Noise renders the jamming outcomes unpredictable. To tackle these challenges, this study formulates the optimization of the RGPO jamming strategy as an adversarial stochastic simulation optimization (ASSO) problem and introduces a novel solution for the white-box RGPO jamming strategy optimization: a local simulation-assisted particle swarm optimization algorithm with an equal resampling scheme (PSO-ER). The PSO-ER algorithm searches for optimal jamming strategies while utilizing a localized simulation of the tracking radar to evaluate the effectiveness of candidate jamming strategies. Experiments conducted on four benchmark cases confirm that the proposed approach is capable of generating well-tuned strategies for white-box RGPO jamming.