雷达有源干扰识别算法综述
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合肥工业大学计算机与信息学院,合肥 230009

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Overview on Recognition Algorithms of Radar Active Jamming
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School of Computer and Information, Hefei University of Technology, Hefei 230009, China

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    摘要:

    在当代电子战中,电子干扰与抗干扰的较量愈演愈烈。开展针对雷达有源干扰的识别算法已经成为雷达对抗领域研究的热点,具有重大的战略意义。本文针对雷达有源干扰识别算法进行整体分析,总结了目前国内外干扰识别手段的一般流程。首先对常见雷达干扰的种类进行划分,详细介绍目前常见的雷达有源干扰信号的干扰机理和信号模型,然后从特征提取手段和分类器的设计两个角度出发全面地梳理干扰识别算法流程。最后针对雷达有源干扰识别算法未来的发展方向做出了展望。

    Abstract:

    In modern electronic warfare, the competition between electronic interference and anti-interference is becoming more and more fierce, which has become a hotspot in the radar countermeasure field to develop the identification algorithms for radar active jamming. This paper analyzes the radar active jamming recognition algorithm in details, and summarizes the general process of jamming identification methods in the world. Firstly, the types of common radar jamming are divided, and the jamming mechanism and the signal model of current common radar active jamming signal are introduced in details. Then from the feature-extraction means and the design of the classifiers, the flow of the jamming identification algorithm are analyzed comprehensively. Finally, the future development directions of the radar active jamming identification algorithms are prospected.

    表 4 FRFT域干扰识别方法汇总Table 4 Summary of jamming recognition methods in FRFT domain
    表 2 双谱域干扰识别方法汇总Table 2 Summary of jamming recognition methods in bispectrum domain
    表 6 不同算法识别效果对比Table 6 Comparison of recognition performance of different algorithms
    表 5 深度学习干扰识别方法汇总Table 5 Summary of deep learning based jamming recognition methods
    图1 干扰识别的一般流程Fig.1 Flow of jamming recognition
    图2 DRFM干扰机原理框图Fig.2 Block diagram of DRFM jammer
    图3 雷达干扰的分类Fig.3 Classification of radar jamming
    图4 雷达被干扰的场景图Fig.4 Scene diagram of radar being jammed
    图5 LFM信号的双谱图及其对角切片Fig.5 Bispectrum and its diagonal slice diagram of LFM signal
    图6 3层小波分解示意图Fig.6 Schematic diagram of three-layer wavelet decomposition
    图7 LFM信号的遍历旋转角度FRFT分析Fig.7 FRFT analysis of ergodic rotation angle of LFM signal
    图8 分类树算法流程图Fig.8 Flow chart of classification tree algorithm
    图9 支持向量与最大间隔Fig.9 Support vector and maximum margin
    图10 卷积神经网络模型图[73]Fig.10 Schematic of CNN model[73]
    表 3 小波域干扰识别方法汇总Table 3 Summary of jamming recognition methods in wavelet domain
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周红平,王子伟,郭忠义.雷达有源干扰识别算法综述[J].数据采集与处理,2022,37(1):1-20

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  • 收稿日期:2021-12-02
  • 最后修改日期:2022-01-14
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  • 在线发布日期: 2022-01-29