基于SAE-GA-SVM模型的雷达新型干扰识别

doi:10.19678/j.issn.1000-3428.0054730

摘要/Abstract

摘要： 针对频谱弥散干扰、切片组合干扰、灵巧噪声干扰、噪声调幅-距离欺骗加性复合干扰与噪声调频-距离欺骗加性复合干扰5种干扰类型的识别问题，提出一种基于SAE-GA-SVM的检测模型算法。建立目标回波与干扰信号的数学模型，采用多域联合的特征提取方法提取47维特征。为有效去除冗余信息并保持较高的识别率，运用深度学习中的稀疏自编码器（SAE），通过SAE结构建立高维空间和低维空间的双向映射，从而获得原始数据的相应最优低维表示。利用遗传算法优化支持向量机的惩罚因子和核函数参数，构建基于SAE-GA-SVM的雷达新型干扰识别检测模型。仿真结果表明，该模型能够有效降低特征维度，相比传统的GA-SVM检测模型识别准确率提高10%。

关键词: 新型干扰, 特征提取, 特征降维, 堆叠自编码器, 遗传算法

Abstract: Aiming at the identification problems that Smeared Spectrum(SMSP),Chopping and Interleaving(C&I),smart noise jamming,the composite jamming of noise amplitude modulation and noise range deception,and the composite jamming of noise frequency modulation and noise range deception.This paper proposes a SAE-GA-SVM-based identification model algorithm,which can identify.The algorithm constructs a mathematical model for target echo and jamming signals,employing a multi-domain joint feature extraction method to extract the 47-dimensional features.In order to effectively remove redundant data and maintain a high identification rate,the Stacked Auto-Encoder(SAE) algorithm in deep learning is adopted.By using the SAE structure,a mutual mapping between high-dimensional space and low-dimensional space is constructed to obtain the corresponding optimal low-dimensional representation of raw data.Then the Genetic Algorithm(GA) is used to optimize the penalty factor and kernel function parameters of Support Vector Machine(SVM),and on this basis the SAE-GA-SVM-based model for new radar jamming identification is established.Simulation results show that the proposed model can effectively reduce the feature dimension,and its classification accuracy is 10% higher than that of the traditional detection models.

Key words: new jamming, feature extraction, feature dimension reduction, Stacked Auto-Encoder(SAE), Genetic Algorithm(GA)

中图分类号:

TN974

罗彬珅, 刘利民, 董健, 刘璟麒. 基于SAE-GA-SVM模型的雷达新型干扰识别[J]. 计算机工程, 2020, 46(6): 281-287.

LUO Binshen, LIU Limin, DONG Jian, LIU Jingqi. Radar New Jamming Identification Based on SAE-GA-SVM Model[J]. Computer Engineering, 2020, 46(6): 281-287.

http://www.ecice06.com/CN/Y2020/V46/I6/281

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