摘要
在短码直扩信号伪码(pseudo-noise,PN)序列的盲估计中,特征值分解(eigenvalue decomposition,EVD)算法、奇异值分解(singular value decomposition,SVD)算法和压缩投影逼近子空间跟踪(projection approximation subspace tracking with deflation,PASTd)算法常被用来估计PN序列。然而,当非同步时延未知时,最大特征值和次大特征值可能相近,此时估计出的最大特征向量实际上是最大特征值和次大特征值对应特征向量的任一非零线性组合,即估计出的最大特征向量存在酉模糊,这会导致从最大特征向量中估计PN序列的算法性能可能很差。针对此问题提出了一种利用协方差矩阵性质估计PN序列的算法。仿真结果表明:所提算法不仅能解决非同步时延未知时估计PN序列算法性能可能很差的问题,还能在低信噪比下获得良好的估计性能。
In the blind estimation of short-code direct sequence spread spectrum( DSSS) signal pseudo-noise( PN) sequences,the eigenvalue decomposition( EVD) algorithm,the singular value decomposition( SVD) algorithm and the projection approximation subspace tracking with deflation( PASTd) algorithm are often used to estimate the PN sequence. However,when the asynchronous time delay is unknown,the largest eigenvalue and the second largest eigenvalue may be very close,resulting in the estimated largest eigenvector being any non-zero linear combination of the really required largest eigenvector and the really required second largest eigenvector. In other words,the estimated largest eigenvector has the unitary ambiguity. This makes the performance of the algorithm estimating the PN sequence from the estimated largest eigenvector very poor. To tackle this problem,a spreading sequence blind estimation algorithm based on the covariance matrix was proposed.Simulation results show that the proposed algorithm not only solves the problem of estimating the PN sequence when the largest eigenvalue and the second largest eigenvalue are close,but also performs well at a low SNR.
作者
李科军
高勇
LI Kejun;GAO Yong(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2020年第2期140-146,共7页
Journal of Chongqing University of Technology：Natural Science
基金
中央高校基本科研业务费专项资金资助项目
