Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder

GUO Limin; KOU Yunhan; CHEN Tao; ZHANG Ming

doi:10.11999/JEIT170588

Volume 40 Issue 4

Apr. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(4): 875-881

GUO Limin, KOU Yunhan, CHEN Tao, ZHANG Ming. Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder[J]. Journal of Electronics & Information Technology, 2018, 40(4): 875-881. doi: 10.11999/JEIT170588

Citation:

GUO Limin, KOU Yunhan, CHEN Tao, ZHANG Ming. Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder[J]. Journal of Electronics & Information Technology, 2018, 40(4): 875-881. doi: 10.11999/JEIT170588

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Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder

doi: 10.11999/JEIT170588

Funds:

The National Natural Science Foundation of China (61571146), The Fundamental Research Funds for the Central Universities (HEUCFP201769)

Received Date: 2017-06-19
Rev Recd Date: 2017-11-21
Publish Date: 2018-04-19

Abstract

Abstract

In order to solve the problem that the correct recognition rate of Low Probability of Intercept (LPI) radar signal is low and the feature extraction is difficult, an automatic classification and recognition system based on Choi-Williams Distribution (CWD) and stacked Sparse Auto-Encoder (sSAE) is proposed. The system starts from the time-frequency image which reflects the essential characteristics of the signal. Firstly, the CWD is performed on the LPI radar signal to obtain the two-dimensional time-frequency image. Then, the obtained time-frequency original image is preprocessed and the preprocessed image is sent into the multilayer SAE for off-line training. Finally, the feature automatically extracted from the SAE is sent to the softmax classifier, to achieve on-line classification and identification of the radar signal. Simulation results show that the classification system achieves overall correct recognition rate of 96.4% at SNR of for the eight LPI radar signals (LFM, BPSK, Costas, Frank and T1~T4), which is better than the method of manually designing the extract signal characteristics under low SNR conditions.

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References

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