一种基于多重特征融合的信源个数估计方法

doi:10.19678/j.issn.1000-3428.0056900

摘要/Abstract

摘要： 针对加权盖尔圆估计准则不能充分利用增广加权盖尔圆矩阵信息的不足，在该准则基础上提出一种融合多重特征的信源个数估计方法。利用阵列天线的接收信号构建增广加权盖尔圆矩阵，从中获取用于描述信源个数的盖尔圆心值、盖尔圆半径和加权盖尔圆半径等多重特征构建高维特征向量，并将其标记后代入支持向量机中，训练可进行信源个数估计的分类器数学模型。实验结果表明，该方法不仅能够在信源数只比阵元数少一个的情况下准确估计信源个数，其在低信噪比和小快拍数的环境下也同样具有良好性能。

关键词: 加权盖尔圆估计准则, 增广加权盖尔圆矩阵, 加权盖尔圆半径, 支持向量机, 信源个数估计

Abstract: The Weighted Gerschgorin Disk Estimation (WGDE) criterion can not make full use of the information of the augmented weighted Gerschgorin disk matrix.Based on WGDC,this paper proposes a source number estimation method by fusing multiple features.The augmented weighted Gerschgorin disk matrix is constructed using the received signals of the array antennas,from which multiple features such as the Gerschgorin disk center value,the Gerschgorin disk radius and the weighted Gerschgorin disk radius,which can be used to describe the number of sources,are simultaneously obtained to construct high-dimensional feature vectors.Multiple feature vectors are labeled and substituted into a Support Vector Machine(SVM) to train a mathematical model of classifier that is capable of estimating the number of sources.Experimental results show that the proposed method can effectively estimate the number of sources when the number of sources is only one fewer than the number of array elements,and the method also has excellent performance in an environment with low Signal-to-Noise Ratio(SNR) and small snapshot.

Key words: Weighted Gerschgorin Disk Estimation(WGDE) criterion, augmented weighted Gerschgorin disk matrix, weighted Gerschgorin disk radius, Support Vector Machine(SVM), source number estimation

中图分类号:

TP393

张冰玉, 潘晴, 田妮莉, Everett Xiaolin Wang. 一种基于多重特征融合的信源个数估计方法[J]. 计算机工程, 2021, 47(4): 115-119,126.

ZHANG Bingyu, PAN Qing, TIAN Nili, Everett Xiaolin Wang. A Source Number Estimation Method Based on Multiple Feature Fusion[J]. Computer Engineering, 2021, 47(4): 115-119,126.

https://www.ecice06.com/CN/Y2021/V47/I4/115

图/表 3

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