【摘要】：在短碼直擴(kuò)信號(hào)偽碼序列的估計(jì)中,當(dāng)使用特征值分解(eigenvalue decomposition,EVD)算法、奇異值分解(singular value decomposition,SVD)算法和壓縮投影逼近子空間跟蹤(projection approximation subspace tracking with deflation,PASTd)算法來估計(jì)偽碼序列時(shí),存在著當(dāng)最大特征值和次大特征值相近時(shí)最大特征向量會(huì)受到干擾,進(jìn)而影響偽碼序列估計(jì)的問題。針對(duì)此問題,提出了一種基于正交特性的偽碼序列估計(jì)算法。在已知碼片速率和偽碼周期的前提下,該算法首先把接收信號(hào)劃分成長(zhǎng)度為兩倍碼元寬度、數(shù)據(jù)重疊50%的數(shù)據(jù)段,然后用SVD估計(jì)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,可以利用兩者的正交特性來估計(jì)擴(kuò)頻序列。該算法不但能在信號(hào)失步時(shí)間未知的情況下估計(jì)偽碼序列,而且仿真結(jié)果表明該算法具有穩(wěn)定性高,需要的數(shù)據(jù)量少和能在低信噪比下有較好的估計(jì)性能等優(yōu)點(diǎn)。
[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.
【作者單位】： 四川大學(xué)電子信息學(xué)院;
【基金】：中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(2082604194194)資助課題
【分類號(hào)】：TN911.23;TN914.42

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