基于RBF神经网络的雷达有源压制干扰识别  

Active Suppression Jamming Recognition Based on RBF Neural Network

作　　者：戴少怀 杨革文 郁文 吴向上 DAI Shaohuai;YANG Gewen;YU Wen;WU Xiangshang(Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China)

机构地区：[1]上海机电工程研究所,上海201109

出　　处：《空天防御》2022年第1期102-107,共6页Air & Space Defense

摘　　要：为解决现代电子战中雷达对干扰类型进行识别时,由于信号特征选取不合适而导致的识别准确率低的问题,提出一种基于时频域分析的径向基函数(radio basis function,RBF)神经网络的干扰识别方法。该方法提出结合干扰信号的时频域特征,利用RBF神经网络收敛速度快、非线性拟合能力强的优势,提高对雷达有源压制干扰的识别概率。仿真结果表明,基于时频域分析的RBF神经网络能够保证较高的干扰识别概率。In order to solve the problem of low recognition accuracy due to improper selection of signal characteristics in the process of jamming recognition by radar in modern electronic warfare,a jamming recognition method based on RBF neural network combined time-frequency domain analysis is proposed.This method proposes to combine the time-frequency domain characteristics of the jamming signal and take the advantage of the fast convergence speed and strong nonlinear fitting ability of the RBF neural network to improve the recognition probability of radar against active suppression jamming.The simulation results show that the RBF neural network based on time-frequency domain analysis can guarantee a high jamming recognition probability.

关 键 词：有源压制干扰 时频域分析 RBF神经网络 识别概率 

分 类 号：TN974[电子电信—信号与信息处理]