关键词: 有源干扰识别, 核主成分分析, 堆叠自编码器, 反向传播神经网络, 特征提取, 特征降维

Abstract:

To solve the problem of low recognition accuracy of radar new active jamming in strong noise environment， a KPCA-SAE-BP network algorithm is proposed. The features of jamming signal in time domain， frequency domain， waveform domain， wavelet domain and bispectral domain are extracted to construct 67-dimensional input space， and the high-dimensional data is nonlinear dimensionality reduction and reconstruction through kernel principal component analysis （KPCA）. A SAE-BP neural network is then used for classification and recognition. Simulation experiments show that in strong noise environment where JNR is greater than -1dB， the recognition accuracy of the KRPA-SAE-BP network algorithm for six new active Jamming is more than 90%， and the training and recognition time is less than 0.7 seconds. Compared with classical BP neural network， SAE-BP network， PCA-BP network and GA-BP network， it has better detection and recognition performance.

Key words: active jamming recognition, kernel principal components analysis（KPCA）, stacked auto encoder（SAE）, backpro pagation（BP） neural network, feature extraction, feature dimension reduction