基于改进SVM的卫星导航信号捕获峰值检测方法  

A peak detection method for satellite navigation signal acquisition based on improved SVM

作　　者：刘兆博 辜晓波 邱泽扬 王名为 LIU Zhaobo;GU Xiaobo;QIU Zeyang;WANG Mingwei(School of Automation,Guangdong University of Technology,Guangzhou Guangdong 510006,China;School of Integrated Circuits,Guangdong University of Technology,Guangzhou Guangdong 510006,China;Techtotop Microelectronics Technology Co.,Ltd,Guangzhou Guangdong 510663,China)

机构地区：[1]广东工业大学自动化学院,广东广州510006 [2]广东工业大学集成电路学院,广东广州510006 [3]泰斗微电子科技有限公司,广东广州510663

出　　处：《太赫兹科学与电子信息学报》2025年第2期123-131,144,共10页Journal of Terahertz Science and Electronic Information Technology

基　　金：国家自然科学基金资助项目(62101138);广东省自然科学基金资助项目(2022A1515012573)。

摘　　要：针对卫星导航接收机在动态场景或导航信号强度较弱场景下,信号捕获峰值检测阈值设定困难以及捕获准确率下降的问题,提出一种基于改进支持向量机(SVM)的卫星导航信号捕获峰值检测方法。该方法首先通过主成分分析(PCA)对样本特征进行降维,然后对卫星导航信号的捕获相关结果进行分类,最后通过判断其相关结果是否存在峰值来确定导航信号是否成功捕获。仿真结果表明,相较于现有的传统阈值设定方法、标准SVM方法以及逻辑回归分类学习方法,本文提出的检测方法具有虚警率低、实警率高的优势,且捕获成功率也优于现有方法。In response to the difficulties in setting the peak detection threshold for signal acquisition and the decrease in acquisition accuracy of satellite navigation receivers in dynamic scenarios or scenarios with weak navigation signal strength,a satellite navigation signal acquisition peak detection method based on improved Support Vector Machine(SVM)is proposed.This method first reduces the dimensionality of sample features through Principal Component Analysis(PCA),then classifies the acquisition correlation results of satellite navigation signals,and finally determines whether the navigation signal is successfully acquired by judging whether there is a peak in the correlation results.Simulation results show that,compared with existing traditional threshold setting methods,standard SVM methods,and logistic regression classification learning methods,the detection method proposed in this paper has the advantages of low false alarm rate and high true alarm rate,and the acquisition success rate is also better than existing methods.

关 键 词：支持向量机 信号捕获 峰值检测 主成分分析(PCA) 全球导航卫星系统(GNSS) 

分 类 号：TN914.42[电子电信—通信与信息系统]