Radar signal modulation recognition method based on synchro-extracting transform denoising

被引：0

作者：

Deng, Zhian ^{[1
,2
]}

Wang, Zhiguo ^{[1
,2
]}

Wang, Sheng'ao ^{[3
]}

Si, Weijian ^{[1
,2
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin

[3] Department of Science and Information Technology, Sichuan Jiuzhou Investment Holding Group Company Limited, Mianyang

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 10期

关键词：

denoising; instaneous frequency estimation; radar signal modulation recognition; synchro-extracting transform;

D O I：

10.12305/j.issn.1001-506X.2024.10.11

中图分类号：

学科分类号：

摘要：

Due to the insufficient time-frequency focusing ability of the existing Cohen-class time-frequency distribution and low modulation recognition accuracy under low signal to noise ratio (SNR) , a grouped convolutional neural network modulation recognition method based on synchronous extracting transform (SET) denoising is proposed. Firstly, SET is used for time-frequency analysis of the radar signals, providing better time-frequency focusing and computational efficiency of time-frequency analysis. Then, the Viterbi algorithm is utilized to search and estimate the instaneous frequency trajectory in the time-frequency coefficient matrix, taking into account the distribution of signal energy intensity and the smoothness of the instaneous frequency trajectory. At the same time, a median filter is applied to remove pulse noise from the obtained instaneous frequency trajectory, and the time-frequency coefficients in the vicinity of the instaneous frequency trajectory are retained to achieve time-frequency image denoising. Finally, the denoised time-frequency images are sent to a grouped convolution neural network with residual connections for feature extraction and modulation recognition. The experimental results demonstrate that, when the SNR is -12 dB, the denoised SET time-frequency images have good time-frequency focusing, and the modulation recognition accuracy is improved by 13. 69% compared to the recognition accuracy without denoising. The proposed radar signal modulation recognition method exhibits excellent recognition performance for various complex modulation types of signals under low SNR conditions. © 2024 Chinese Institute of Electronics. All rights reserved.

引用

页码：3334 / 3346

页数：12