基于BAS-BPNN的调频无线电引信目标与扫频干扰识别方法  

Recognition Method of Target and Sweep Jamming Signal for FM Radio Fuze Based on BAS-BPNN

作　　者：刘冰[1] 郝新红[1] 周文 杨瑾[1] LIU Bing;HAO Xinhong;ZHOU Wen;YANG Jin(Science and Technology on Electromechanical Dynamic Control Laboratory,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京理工大学机电动态控制重点实验室,北京100081

出　　处：《兵工学报》2023年第8期2391-2403,共13页Acta Armamentarii

基　　金：国家自然科学基金项目(61871414)。

摘　　要：针对调频无线电引信在复杂电磁战场环境对抗调幅扫频式干扰能力不足的问题,提出一种基于频域信息熵、范数熵和倒频谱熵的调频无线电引信目标识别方法。基于目标和调幅扫频干扰作用下的调频无线电引信检波端输出信号,提取频域信息熵、范数熵和倒频谱熵构建特征矩阵,并利用天牛须搜索(BAS)算法对反向传播神经网络(BPNN)初始权重值和阈值进行优化,利用优化后的BPNN对目标和调幅扫频干扰信号进行分类识别。实测数据实验结果表明,特征提取方法构成的特征矩阵在目标与干扰之间具备可分性,BAS算法优化获得最优参数的BPNN时,该分类器的识别准确率可以达到99.96%,显著提升了调频无线电引信对抗调幅扫频干扰的能力。In order to solve the problem that the FM radio fuze is unable to counter AM frequency sweep jamming signals on battlefield in a complex electromagnetic environment,a target recognition method based on frequency domain information entropy,norm entropy and cepstrum entropy is proposed.Based on the output signal of the FM radio fuze under the action of the target and AM frequency sweep jamming signal,the frequency information entropy,norm entropy and cepstrum entropy are extracted to construct the feature matrix.The BAS algorithm is used to optimize the initial weight values and threshold of the back propagation neural network(BPNN).Then the optimized BPNN is used to classify and recognize the target and AM frequency sweep jamming signal.The experimental results with the measured data show that the feature matrix formed by feature extraction has separability between the target and the jamming signal.When the BPNN with optimal parameters is obtained by the optimization of the BAS algorithm,the recognition accuracy of the classifier can reach 99.96%,which significantly improves the ability of the FM radio fuze to counter AM frequency sweep jamming signals.

关 键 词：调频无线电引信 熵特征 目标识别 反向传播神经网络 天牛须算法 

分 类 号：TJ43[兵器科学与技术—火炮、自动武器与弹药工程]