Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

With the increase of radar signal modulations and the emergence of new system radars, the receiver will intercept radar signals at the same time. In order to accurately estimate and suppress the signals, this paper proposes an accurate recognition system for radar emitter signals. The system can effectively separate multiple signals and accurately recognize Binary Phase Shift Keying (BPSK), Linear Frequency Modulation (LFM), Continuous Wave (CW), Costas, Frank code, and P1 to P4 codes. The separation technique based on fractional Fourier transform is proposed to decompose received signals into multiple components. Furthermore, a transferable GoogleNet is explored to achieve accurate recognition of the first component with better separation effect. Meanwhile, variational mode decomposition is developed to eliminate the noise of the second component; then, the fusion features are extracted to improve the recognition rate of the second component. Finally, the improved particle swarm optimization algorithm is proposed to find best support vector machine parameters. The simulation results show that the recognition rate of single signal and double signals can reach 96.23% and 72%, respectively, when signal-to-noise ratio is 0 dB. The system can also bring some inspiration to medical and mechanical signal recognition.