Strategy Optimization for Range Gate Pull-Off Track-Deception Jamming Under Black-Box Circumstance

Abstract

In this paper, we study the strategy optimization problem of black-box range gate pull-off (RGPO) jamming. In the black-box RGPO jamming, the jammer does not have extensive knowledge about the tracking model of the threat radar, which makes it difficult to accurately estimate the performance of candidate jamming strategies. To address the issue, this paper proposes a multi-mode black-box RGPO jamming method, which uses a set of local elemental simulation models to cover the possible tracking model of the threat radar, and the overall performance estimate of the candidate jamming strategies are obtained by a certain combination of the sampling results from the local elemental simulation models. To obtain the desired RGPO jamming strategy, a particle swarm optimization with optimal computing budget allocation scheme (PSO-OCBA) based multi-mode black-box RGPO jamming strategy optimization algorithm is proposed. In addition, we construct several most widely used tracking problems as the benchmark problems to test the performance of the proposed method. Experimental results demonstrate that the proposed method is highly competitive to deal with the strategy optimization problem of the black-box RGPO jamming.