用于扩频码型估计的改进自相关矩阵构造算法  

To improve the estimation of spread spectrum code autocorrelation matrix construction algorithm

作　　者：程皓[1] 喻娜[1] 罗正华[1] 

机构地区：[1]成都大学电子信息工程学院,四川成都610106

出　　处：《微型机与应用》2014年第19期83-86,共4页Microcomputer & Its Applications

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。提出了自相关矩阵的新型构造算法。该算法构造的自相关矩阵,特征值分解后其对噪声不敏感,克服了常规子空间方法的弱点。仿真试验表明,该方法应用在高噪声、低信噪比的实际通信环境下,特征值不会被噪声湮没,从根本上解决了传统子空间分辨率不足的问题。同时,仿真表明,该方法对于多用户扩频信号同样适用,可解决多用户扩频信号的码元分离问题,其计算结果与理论计算一致,验证了算法的正确性。Subspace theory is used in the singular value decomposition and eigenvalue decomposition of the correlation matrix, usually expressed as the product of its own transpose the received vector. This paper proposed a new algorithm of correlation matrix. Auto-correlation matrix constructed by this algorithm, eigenvalue decomposition is not sensitive to noise, overcomes the weakness of conventional subspace methods. Simulation results show that, the method is applied in high noise, low signal-to-noise ratio of the actual communication environment, values will not be noise annihilation, solve the sub problem of inadequate spatial resolution fundamentally. At the same time, simulation results show that this method is applicable to the multi user spread spectrum signal, and that symbols can solve the multi user spread spectrum signal separation problem. The calculation results and the theoretical calculation are consistent. This verifies the correctness of the algorithm.

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]