Radar emitter recognition based on the deep learning of time-frequency feature

被引：0
|
作者：
Li D. [1 ]
Yang R. [1 ]
Dong R. [1 ]
机构：
[1] Early Warning Intelligence Department, Air Force Early Warning Academy, Wuhan
来源：
Yang, Ruijuan (ruijuany@sohu.com) | 1600年 / National University of Defense Technology卷 / 42期
关键词：
Convolutional neural network; Deep learning; Down sampling of short time Fourier transfer; Radar emitter recognition; Time-frequency feature;
D O I：
10.11887/j.cn.202006014
中图分类号：
学科分类号：
摘要：
Aiming at the problem of insufficient expansion ability and low recognition rate in radar emitter recognition, an intelligent recognition algorithm based on the deep learning of time-frequency feature was proposed. The shallow two-dimensional time-frequency features with high recognition and stability were quickly extracted by down sampling of short-time Fourier transform, and the noise reduction and other pre-processing were completed by using the sparseness of the local frequency-domain signal; a convolutional neural network for deep feature learning and recognition was designed, and the scale of the network was expanded by different scale convolution kernels to enhance the feature representation ability; the network was trained and tuned by using eight kinds of emitter signals under high SNR(signal-to-noise ratio) conditions, and the effectiveness of the algorithm and network was verified by a low SNR sample. The experimental results showed that the system achieves overall recognition rate of 98.31% at SNR of -8 dB, which verifies that the proposed algorithm has strong robustness. © 2020, NUDT Press. All right reserved.
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页码：112 / 119
页数：7
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