基于信息几何去噪的改进SVM的通信信号识别  

Improved SVM Communication Signal Recognition Based on Information Geometry Denoising

作　　者：程雨晴 郭沐然 王乐萍 Cheng Yuqing;Guo Muran;Wang Leping(College of Information and Communication Engineering,Harbin Engineering University,Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology,Harbin 150001,China;College of Communication Engineering,Army Engineering University of PLA,Nanjing 210000,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院、先进船舶通信与信息技术工业和信息化部重点实验室,哈尔滨150001 [2]陆军工程大学通信工程学院,南京210000

出　　处：《航空兵器》2023年第5期121-126,共6页Aero Weaponry

基　　金：国家自然科学基金项目(62001136)。

摘　　要：针对传统人工提取特征进行通信信号识别准确率低的问题,本文在支持向量机(SVM)的基础上,提出了一种基于信息几何去噪的改进SVM的识别方法。该方法通过Choi-Williams分布(CWD)时频变换获得不同通信信号的时频图像,然后利用能够更加准确衡量像素点之间差异性的测地线距离实现时频图像的去噪,进而利用AlexNet卷积神经网络对时频图进行特征提取,并基于信息几何改进的SVM对通信信号进行分类,实现了有效分类识别。仿真结果表明,该方法在0 dB信噪比(SNR)下,识别率仍然能够达到97%以上,除此之外,该方法在小样本的情况下仍然有效。Aiming the problem of low accuracy of communication signal recognition by traditional manual feature extraction,an improved SVM recognition method based on information geometry denoising is proposed exploiting the support vector machine(SVM).The proposed method obtains the time-frequency images of different communication signals through the Choi-Williams distribution(CWD)time-frequency transform,and uses the geometric ground distance to accurately measure the difference between pixels for denoising.Then,the AlexNet is used to extract features from the time-frequency maps.Finally,by using the improved SVM based on the information geometry,the classification of communication signal is made to achieve effective classification and recognition.The simulation results show that the recognition rate of the proposed method achieves more than 97%at 0 dB signal-to-noise ratio(SNR).In addition,the method is still effective in the case of small samples.

关 键 词：信息几何 图像去噪 通信信号 调制识别 支持向量机(SVM) 测地线距离 AlexNet 

分 类 号：TJ760[兵器科学与技术—武器系统与运用工程]