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Abstract：
The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method\'s performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.
摘要：
主动干扰和其他雷达的相互干扰严重损害了雷达的检测性能。本文提出了一种无线电信号调制识别方法来准确识别这些信号,这有助于干扰取消决定。基于元特征增强改进的集成学习堆叠算法,所提出的方法采用随机森林，K-最近的邻居,和高斯朴素贝叶斯作为基础学习者，逻辑回归作为元学习者。它以信号的多域特征作为输入，包括模糊熵在内的时域特征，斜率熵，和Hjorth参数；频域特征，包括谱熵；和分形域特征，包括分形维数。模拟实验,包括雷达和有源干扰的七种常见信号类型，进行有效性验证和性能评估。结果证明了该方法相对于其他分类方法的性能优势,以及其满足低信噪比和少射学习要求的能力。
