A Two-Stage Optimization Framework for Radar Jamming Effectiveness Evaluation

In complex electromagnetic environments, radar signals intercepted by jammers often contain biased data due to factors such as radar mode switching, electromagnetic interference, and receiver noise. To address this challenge, this paper proposes a two-stage optimization framework for jamming effect evaluation from the jammerâ s perspective. In the first stage, a pre-evaluation is conducted using an entropy-optimized K-means discretization algorithm (KDEOA) to adaptively partition pulse descriptor word (PDW) parameters, enhancing robustness against noise. A GCSAO-LSSVM model is then employed to improve classification accuracy through optimal parameter tuning and a periodic oscillation mutation strategy. In the second stage, an improved entropy weight method (IEWM) integrating Tsallis entropy, kernel density standardization, and game theory is used for objective weighting, followed by an enhanced TOPSIS method (ITOPSIS) incorporating interquartile range standardiza-tion and dynamic ideal solution fusion for quantitative scoring. Experimental results demonstrate that the pro-posed framework achieves the highest pre-evaluation accuracy across all noise levels (up to 50% contamination), with IEWM exhibiting the lowest weight variation rate (0.11â 0.23%) and ITOPSIS showing the strongest correlation (0.7290) with baseline scores under high noise. The main limitations include sensitivity to severe signal distortion and assumption of stable radar behavior. This approach enables accurate, non-cooperative jamming assessment and supports robust decision-making in cognitive electronic warfare.