Abstract:

Aiming at the requirements of adaptive adjustment of jamming strategies according to radar behaviors and real-time evaluation during radar countermeasures， an evaluation method based on the fusion of AdaBoost algorithm and Naive Bayes classifier is proposed to meet the requirements of adaptive adjustment and real-time evaluation of interference strategy with radar behavior during radar countermeasures. This method extracts features from the time domain， frequency domain， modulation domain， and polarization domain of radar signals， using a naive Bayes classifier as the base classifier. Through AdaBoost iteration and dynamic feedback scaling adjustment of difficult to distinguish sample weights， the accuracy and stability of the model evaluation ability are enhanced. Simulation experiments have shown that the performance of this algorithm is superior to typical algorithms. This study provides an optimized solution for real-time quantitative evaluation of radar interference effects in complex electromagnetic environments， which can be applied to intelligent interference decision-making in radar countermeasures.

Key words: AdaBoost, naive Bayes, radar interference, effectiveness evaluation, radar behavior, interference with decision-making

摘要：

针对雷达对抗过程中干扰策略随雷达行为自适应调整与实时评估需求，提出一种基于AdaBoost算法与朴素贝叶斯分类器融合的评估方法。该方法提取雷达信号的时域、频域、调制域和极化域特征，以朴素贝叶斯分类器为基分类器，通过AdaBoost迭代和动态反馈缩放调整难分样本权重，增强模型评估能力的准确性和稳定性，仿真实验证明该算法性能优于典型算法。本研究为复杂电磁环境下雷达干扰效果的实时量化评估提供了一种优化的解决方案，可应用于雷达对抗中的智能干扰决策。

关键词: AdaBoost, 朴素贝叶斯, 雷达干扰, 效能评估, 雷达行为, 干扰决策