A Radar Signal Recognition System Based on Non-Negative Matrix Factorization Network and Improved Artificial Bee Colony Algorithm

The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signal-to-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at −4 dB, 99.82% at 6 dB.