Radar Emitter Signal Recognition Based on Dilated Residual Network

被引：0
|
作者：
Qin X. [1 ]
Huang J. [1 ]
Zha X. [1 ]
Luo L.-P. [1 ]
Hu D.-X. [1 ]
机构：
[1] PLA Strategic Support Force Information Engineering University, Zhengzhou, 450001, Henan
来源：
| 1600年 / Chinese Institute of Electronics卷 / 48期
关键词：
Deep learning; Dilated residual network; Image pre-processing; New system radar; Radar signal recognition; Time-frequency analysis;
D O I：
10.3969/j.issn.0372-2112.2020.23.006
中图分类号：
学科分类号：
摘要：
This paper proposes a radar emitter signal recognition method based on time-frequency analysis and dilated residual network (DRN) to solve the problem of difficulty in feature extraction and low accuracy in recognition of complex multiple radar emitter signals under low signal-to-noise ratio (SNR).Firstly, the signal time-domain waveform is transformed into a two-dimensional time-frequency image by time-frequency analysis to reflect the essential characteristics of signal.Then the time-frequency image pre-processing is carried out to retain the time-frequency image complete information and adapt to the deep learning model input.Finally, the DRN is constructed to automatically extract the signal time-frequency image features and realize the recognition of radar emitter signal.Experimental results show that when the SNR is -6dB, the overall recognition rate of the proposed method for 16 types of radar signals can reach 98.2%, and the overall recognition rate for time-frequency image similar to linear frequency modulation (LFM) signals is more than 95%.In this paper, a new intelligent recognition method for radar emitter signal is presented, which has nice engineering application prospects. © 2020, Chinese Institute of Electronics. All right reserved.
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页码：456 / 462
页数：6
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