Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

The development of cognitive radio and radar electronic reconnaissance has put forward an important demand for improving the recognition ability of modulated signals in complex electromagnetic environment. In this paper, we propose a valid radar signal modulation recognition technology under low signal-to-noise ratio (SNR). The recognition technology can recognize 12 different modulation signals, including Costas, LFM, NLFM, BPSK, P1-P4, and T1-T4 codes. First, we propose the image fusion algorithm of non-multi-scale decomposition to fuse images of a single signal with different time-frequency (T-F) methods. Specifically, weights are designed by the principal component analysis, which could combine significative details of T-F images. Second, we adopt transfer learning-based convolutional neural networks and self-training-based stacked autoencoder, which extract the effective information on fusion image, furthermore guarantee the recognition performance. Moreover, multi-feature fusion algorithm is used to fuse features, which reduces redundant information on features and enhances computing efficiency. Finally, the classifier is performed by a classical algorithm called support vector machine. Simulation results show that the average recognition success rate is 95.5% at SNR of -6dB. It is testified that proposed recognition technology possesses good robustness and superiority in RSR with a wide range of SNR.