同步提取变换去噪的雷达信号调制识别方法

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

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (10): 3334-3346.doi: 10.12305/j.issn.1001-506X.2024.10.11

• 传感器与信号处理 • 上一篇

同步提取变换去噪的雷达信号调制识别方法

邓志安¹^,², 王治国¹^,², 王盛鳌³^,*, 司伟建¹^,²

1. 哈尔滨工程大学信息与通信工程学院, 黑龙江哈尔滨 150001
2. 先进船舶通信与信息技术工业和信息化部重点实验室, 黑龙江哈尔滨 150001
3. 四川九洲投资控股集团有限公司科技与信息化部, 四川绵阳 621000

收稿日期:2023-06-10 出版日期:2024-09-25 发布日期:2024-10-22
通讯作者: 王盛鳌
作者简介:邓志安 (1985—), 男, 教授, 博士, 主要研究方向为人工智能、宽带信号处理
王治国 (1999—), 男, 硕士研究生, 主要研究方向为雷达信号调制识别
王盛鳌 (1983—), 男, 高级工程师, 硕士, 主要研究方向为信号处理与目标识别
司伟建 (1971—), 男, 研究员, 博士, 主要研究方向为宽带信号处理、电子侦察
基金资助:
国家自然科学基金(61971155);中央高校基本科研业务费(3072022TS0802)

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

Zhian DENG¹^,², Zhiguo WANG¹^,², Sheng'ao WANG³^,*, Weijian SI¹^,²

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 150001, China
3. Department of Science and Information Technology, Sichuan Jiuzhou Investment Holding Group Company Limited, Mianyang 621000, China

Received:2023-06-10 Online:2024-09-25 Published:2024-10-22
Contact: Sheng'ao WANG

摘要/Abstract

摘要：

针对已有Cohen类时频分布等方法时频聚焦能力不足、在低信噪比(signal to noise ratio, SNR)情况下调制识别准确率低的问题, 提出一种基于同步提取变换(synchro-extracting transform, SET)去噪的分组卷积神经网络调制识别方法。所提方法使用SET对雷达信号进行时频分析, 以获得良好的时频聚焦性, 提高时频分析的计算效率; 通过Viterbi算法搜索估计时频系数矩阵中的瞬时频率轨迹, 综合考虑信号能量强度分布与瞬时频率轨迹的平滑性, 并对得到的瞬时频率轨迹进行中值滤波以去除脉冲噪声; 保留瞬时频率轨迹邻域的时频系数, 以达到时频图去噪的目的。最后, 将去噪后的时频图送入具有残差连接的分组卷积神经网络进行特征提取与调制识别。实验结果表明, 当SNR为-12 dB时, 去噪后的SET时频图时频聚焦性好, 调制识别准确率比未去噪的识别准确率提高了13.69%, 证明所提出的雷达信号调制识别方法在低SNR条件下对多种复杂调制类型的信号具有良好的识别性能。

关键词: 雷达信号调制识别, 同步提取变换, 瞬时频率估计, 去噪

Abstract:

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.

Key words: radar signal modulation recognition, synchro-extracting transform, instaneous frequency estimation, denoising

中图分类号:

TN957.51

邓志安, 王治国, 王盛鳌, 司伟建. 同步提取变换去噪的雷达信号调制识别方法[J]. 系统工程与电子技术, 2024, 46(10): 3334-3346.

Zhian DENG, Zhiguo WANG, Sheng'ao WANG, Weijian SI. Radar signal modulation recognition method based on synchro-extracting transform denoising[J]. Systems Engineering and Electronics, 2024, 46(10): 3334-3346.

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信号长度N	方法
信号长度N	SET	CWD	SPWVD
512	44.7	155.5	179.1
768	93.1	343.1	385.9
1 024	176.3	673.5	747.1

信号调制类型	训练集和验证集参数	测试集参数
BPSK	载频: 5~40 MHz	载频: 5~45 MHz
	Baker码长度: {5、7、11、13}	Baker码长度: {5、7、11}
	码元宽度: 500~800 ns	码元宽度: 400~700 ns
	相位循环次数: 2~5	相位循环次数: 4~6
CW	载频: 5~40 MHz	载频: 5~45 MHz
CW	脉宽: 6.4~13.6 μs	脉宽: 4.8~11.2 μs
DLFM、EQFM、LFM、SFM	中心频率: 5~30 MHz	中心频率: 10~35 MHz
	带宽: 10~20 MHz	带宽: 15~30 MHz
	脉宽: 6.4~13.6 μs	脉宽: 4.8~11.2 μs
2FSK	2个载频: 5~40 MHz	2个载频: 5~45 MHz
	码元宽度: 1.5~2 μs	码元宽度: 1.2~1.6 μs
	码元数量: 4~8	码元数量: 6~12
4FSK	4个载频: 5~40 MHz	4个载频: 5~45 MHz
	码元宽度: 1.5~2 μs	码元宽度: 1.2~1.6 μs
	码元数量: 8~12	码元数量: 10~16
P1码、P2码、P3码、P4码、Frank	载频: 5~40 MHz	载频: 5~45 MHz
	相位跳变数: (5~8)²	相位跳变数: (5~8)²
	码元宽度: 100~600 ns	码元宽度: 200~700 ns

分组数d	模型参数量(M)	识别精度(SNR=-12 dB)/%
2	1.309 29	65.35
4	0.866 93	66.60
8	0.645 74	65.61

Stage	模型参数量(M)	识别精度(SNR=-12 dB)/%
[2, 2, 0, 1]	0.305 26	62.46
[2, 2, 2, 1]	0.617 07	64.57
[2, 2, 4, 1]	0.866 93	66.60
[2, 2, 6, 1]	1.116 78	61.22

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同步提取变换去噪的雷达信号调制识别方法

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

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