基于空间平滑算法的改进多重信号分类算法  

Modified Multiple Signal Classification Algorithm Based on Spatial Smoothing Algorithm

作　　者：秦毅 陈向阳[1] QIN Yi;CHEN Xiangyang(School of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan 430205,China)

机构地区：[1]武汉工程大学计算机科学与工程学院,湖北武汉430205

出　　处：《武汉工程大学学报》2023年第4期450-455,共6页Journal of Wuhan Institute of Technology

基　　金：湖北省教育厅计划项目(B2021083);武汉工程大学教研项目(X2015035,X2021029)。

摘　　要：为了提高传统解相干算法的估计精度,提出了一种基于空间平滑算法的改进多重信号分类算法。将空间平滑算法的子阵互相关产生的前向平滑修正矩阵和后向平滑修正矩阵与子阵自相关矩阵互相关产生的前向平滑修正矩阵和后向平滑修正矩阵分别相加,得到新的前向平滑矩阵和后向平滑矩阵,再通过矩阵分解法将新的前向平滑矩阵和后向平滑矩阵进行组合,使用奇异值分解对组合后的矩阵进行分解得到信号的噪声子空间,并通过多重信号分类算法进行估计,最大限度的利用了前向平滑修正矩阵和后向平滑修正矩阵的信息。经过仿真验证,该算法在信号相干条件下能精确地估计入射角度,并且对比现有的解相干算法在信噪比相同条件下估计性能提升了9%。To improve the estimation accuracy of traditional decorrelation algorithms,an modified multiple signal classification algorithm was proposed based on a spatial smoothing algorithm.The forward smoothing correction matrix and the backward smoothing correction matrix,generated by the subarray cross-correlation and cross-correlation of the subarray autocorrelation matrix of the spatial smoothing algorithm,were respectively added to obtain new forward and backward smoothing matrixes,which were then combined by matrix decomposition approach,after that,the combined matrixes were decomposed by singular value decomposition,and the noise subspace of the signals was obtained,which maximizes the information of the forward and backward smoothing correction matrixes through estimation of the multiple signal classification algorithm.It is verified that this algorithm can accurately estimate the incident angles under signal coherence conditions,and its performance is improved by 9%compared with that of the existing decorrelation algorithms under the same signal-to-noise ratio conditions.

关 键 词：解相干 空间平滑 奇异值分解 多重信号分类算法 

分 类 号：TN911[电子电信—通信与信息系统]