Low Probability of Intercept Radar Signal Detection Algorithm Based on Convolutional Neural Networks

JIANG Yilin; YIN Ziru; SONG Yu

doi:10.11999/JEIT210132

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 718-725

JIANG Yilin, YIN Ziru, SONG Yu. Low Probability of Intercept Radar Signal Detection Algorithm Based on Convolutional Neural Networks[J]. Journal of Electronics & Information Technology, 2022, 44(2): 718-725. doi: 10.11999/JEIT210132

Citation:

JIANG Yilin, YIN Ziru, SONG Yu. Low Probability of Intercept Radar Signal Detection Algorithm Based on Convolutional Neural Networks[J]. Journal of Electronics & Information Technology, 2022, 44(2): 718-725. doi: 10.11999/JEIT210132

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Low Probability of Intercept Radar Signal Detection Algorithm Based on Convolutional Neural Networks

doi: 10.11999/JEIT210132

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62071137)

Received Date: 2021-02-05
Rev Recd Date: 2021-07-18

Available Online: 2021-08-10

Publish Date: 2022-02-25

Abstract

Abstract

In order to solve the problem of radar intercepting receiver’s unsatisfactory detection effect on Low Probability of Intercept (LPI) radar signal, a method of LPI radar signal detection based on Convolutional Neural Networks (CNN) is proposed, which defines signal and noise by effective signal pulse width in intercepted signal. The similarity of the convolution kernel and the matched filter in the structure can improve the detection accuracy of the signal under the low SNR.A large number of analog data sets based on four typical LPI radar signals (Linear Frequency Modulation signal (LFM), NonLinear Frequency Modulation signal (NLFM), Binary Phase Shift Keying signal (BPSK), COSTAS frequency coded signal) and white noise signals are used for CNN model training. At the same time, a small amount of measured signals (Linear Frequency Modulation signal (LFM), Binary Phase Shift Keying signal (BPSK)) are added as verification set for adaptation, so as to match better the detection model of measured signals. Finally, the experimental results show that the proposed algorithm has a good detection effect in the case of low SNR, and has the ability to generalize the LPI radar signals under various modulation modes and different SNR.
- Signal detection,
- Low Probability of Intercept (LPI),
- Convolutional Neural Networks (CNN),
- Measured signal,
- Low signal-to-noise ratio

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References(15)

References

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