基于正交特性的短码直扩信号伪码序列盲估计

发布时间：2018-08-31 09:18

【摘要】：在短码直扩信号伪码序列的估计中,当使用特征值分解(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)算法来估计伪码序列时,存在着当最大特征值和次大特征值相近时最大特征向量会受到干扰,进而影响伪码序列估计的问题。针对此问题,提出了一种基于正交特性的伪码序列估计算法。在已知码片速率和伪码周期的前提下,该算法首先把接收信号划分成长度为两倍码元宽度、数据重叠50%的数据段,然后用SVD估计出最大特征向量和次大特征向量,由于最大特征向量和次大特征向量是相互正交的,可以利用两者的正交特性来估计扩频序列。该算法不但能在信号失步时间未知的情况下估计伪码序列,而且仿真结果表明该算法具有稳定性高,需要的数据量少和能在低信噪比下有较好的估计性能等优点。
[Abstract]:When using eigenvalue decomposition (eigenvalue decomposition,EVD) algorithm, singular value decomposition (singular value decomposition,SVD) algorithm and compressed projection approximation subspace tracking (projection approximation subspace tracking with deflation,PASTd) algorithm to estimate the pseudo-code sequence, the pseudo-code sequence is estimated. There is a problem that the maximum eigenvector will be disturbed when the maximum eigenvalue is close to the second largest eigenvalue, which will affect the pseudo code sequence estimation. To solve this problem, a pseudo code sequence estimation algorithm based on orthogonal property is proposed. On the premise of known chip rate and pseudo-code period, the received signal is divided into two symbol widths, and the data overlaps by 50%. Then the maximum eigenvector and the sub-large eigenvector are estimated by SVD. Since the maximum eigenvector and the sub-large eigenvector are orthogonal to each other, the orthogonal properties of the two vectors can be used to estimate the spread spectrum sequence. The algorithm not only can estimate the pseudo code sequence with unknown signal out-of-step time, but also has the advantages of high stability, small amount of data and good estimation performance at low SNR.
【作者单位】：四川大学电子信息学院;
【基金】：中央高校基本科研业务费专项资金(2082604194194)资助课题
【分类号】：TN911.23;TN914.42

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