相关矩阵相乘构造子空间方法  

Correlation Matrix Multiplication Based on Subspace Approach

作　　者：程皓[1] 郭伟[1] 

机构地区：[1]成都电子科技大学国家级通信抗干扰实验室,博士生成都610054

出　　处：《电子测量与仪器学报》2007年第3期28-32,共5页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金资助项目(编号:60472052)

摘　　要：子空间理论中被用于奇异值分解或特征值分解的自相关矩阵,通常可表示为接收向量与其自身转置的乘积。本文提出了一种新的自相关矩阵构造方法。通过对新构造的自相关矩阵的特征值分解,能有效地克服传统子空间方法对噪声敏感的缺点,在低信噪比情况下,能有效解决信号特征值被噪声特征值淹没(分辨率不足)的问题。随后,这种方法被用于直序列扩频信号的扩频码序列估计中。结果显示,该方法仿真效果与理论计算一致。The auto correlation matrix R, used in eigenvalue decomposition （EVD） or singular value decomposition （ SVD）, can be denoted as the product of the received vector and its transposition. A new method of constructing the correlation matrix is proposed in this paper. The proposed algorithm can enhance the signal eigenvalues＇resolving power and resolve the problem that traditional subspace methods cannot be applied in low SNR condition. Then the proposed new method is applied to the signature sequence estimation of direct sequence spread spectrum （DSSS） signal. Its performance is analyzed, and some simulation examples are presented, which show that the simulation results coincide with those of theoretical calculation.

关 键 词：子空间理论 相关矩阵 特征值分解 直序列扩频 

分 类 号：TN911.23[电子电信—通信与信息系统]