Author(s)

Qinghua Hou ¹, Huibin Wu ¹

Affiliation(s)

¹ Henan Polytechnic University, Jiaozuo, China

Corresponding Author

Qinghua Hou

ABSTRACT

Aiming at the problem of low probability of intercept (LPI) radar signal recognition accuracy under low signal-to-noise ratio (SNR), a method for LPI radar signal recognition based on convolutional neural network (CNN) and time-frequency denoising is proposed. Firstly, the Smoothed Pseudo Wigner-Ville Distribution (SPWVD), which performs well under low SNR, is applied for time-frequency analysis of radar signals. Then, a frequency domain filter is designed using the K-means clustering method to reduce noise in the signal. Finally, the basic structure of the CNN network is studied, and a CNN network structure is designed and developed for the proposed LPI radar signal recognition system. Suitable hyperparameters are determined for it through parameter tuning. Time-frequency images are input into the CNN network to extract and learn deep features for radar signal recognition. Experimental results show that when the SNR is -8 dB, the overall recognition accuracy of 12 kinds of LPI radar signals reaches 91.67% using this method.

KEYWORDS

LPI radar signal; SPWVD time-frequency analysis; K-means clustering; Convolutional Neural Network

CITE THIS PAPER

Qinghua Hou, Huibin Wu, Recognition of LPI Radar Signal Intrapulse Modulation Based on CNN and Time-Frequency Denoising. Journal of Electronics and Information Science (2024) Vol. 9: 142-152. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090119.

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