Low Probability of Intercept Radar Signal Recognition Based on Semi-Supervised Support Vector Machine

Author:

Xu Fuhua1,Hu Haoning1,Mu Jiaqing1,Wang Xiaofeng1ORCID,Zhou Fang1,Quan Daying1ORCID

Affiliation:

1. School of Information Engineering, China Jiliang University, Hangzhou 310018, China

Abstract

Low probability of intercept (LPI) radar signal recognition under low signal-to-noise ratio (SNR) is a challenging task within electronic reconnaissance systems, particularly when faced with scarce labeled data and limited resources. In this paper, we introduce an LPI radar signal recognition method based on a semi-supervised Support Vector Machine (SVM). First, we utilize the Multi-Synchrosqueezing Transform (MSST) to obtain the time–frequency images of radar signals and undergo the necessary preprocessing operations. Then, the image features are extracted via Discrete Wavelet Transform (DWT), and the feature dimension is reduced by the principal component analysis (PCA). Finally, the dimensionality reduction features are input into the semi-supervised SVM to complete the classification and recognition of LPI radar signals. The experimental results demonstrate that the proposed method achieves high recognition accuracy at low SNR. When the SNR is −6 dB, its recognition accuracy reaches almost 100%.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Reference29 articles.

1. Automatic intrapulse modulation classification of advanced LPI radar waveforms;Kishore;IEEE Trans. Aerosp. Electron. Syst.,2017

2. LPI radar signal detection based on radial integration of Choi-Williams time-frequency image;Liu;J. Syst. Eng. Electron.,2015

3. Park, D.H., Bang, J.H., Park, J.H., and Kim, H.N. (2022, January 25–30). A Fast and Accurate Convolutional Neural Network for LPI Radar Waveform Recognition. Proceedings of the 19th European Radar Conference (EuRAD) as part of 25th European Microwave Week, Milan, Italy.

4. Fu, Y., and Wang, X. (2017, January 25–26). Radar Signal Recognition Based on Modified Semi-Supervised SVM Algorithm. Proceedings of the 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.

5. Liu, X.K., Cui, S.H., Zhao, C.L., Wang, P.B., and Zhang, R.J. (2018, January 14–16). Bind intra-pulse modulation recognition based on machine learning in radar signal processing. Proceedings of the 7th International Conference on Communications, Signal Processing, and Systems (CSPS), Dalian, China.

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