A Modeling Method for Prior Information Representation of Anti-Active-Jamming

Le résumé fourni par la source

Active jamming signals, which exploit radar-transmitted signals, can significantly degrade radar detection performance. Traditional anti-jamming methods typically require accurate interference parameters; their performance deteriorates substantially when parameter deviations occur. To quantify the impact, this paper proposes a prior-information-based modeling approach for characterizing jamming tolerance. Initially, typical active jamming are suppressed under accurate parameter conditions to analyze the effect of parameter errors. Subsequently, a mapping model between jamming parameter deviations and resultant jamming peak levels of pulse-compressed echoes is established. This is achieved using a Long Short-Term Memory (LSTM) network, whose hyperparameters are optimized via the Particle Swarm Optimization (PSO) algorithm. This model is then used to predict sidelobe performance under various deviation scenarios. Numerical simulations demonstrate that the established model accurately represents the input-output relationship and offers reliable predictive capability. The study provides a quantitative basis for designing parameter tolerance and evaluating the robustness of anti-jamming systems.

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