Multi-Resolution Fusion Convolutional Neural Networks for Intrapulse Modulation LPI Radar Waveforms Recognition

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1 similar to P4, and T1 similar to T4, corrupted with additive white Gaussian noise of SNR from 10 dB to -8 dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6 dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2 dB or below -8 dB can effectively improve the training speed of the network while maintaining recognition accuracy.