This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） dire...This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.展开更多
针对特征分解方法在实现非等功率同步直接序列码分多址(DS-CDMA)信号伪码序列盲估计时存在的处理数据向量不能太长以及不能工作于非平稳环境中的问题,引入了一种由主分量分析实现自适应特征提取的在线无监督学习(LEAP)神经网络(NN)。首...针对特征分解方法在实现非等功率同步直接序列码分多址(DS-CDMA)信号伪码序列盲估计时存在的处理数据向量不能太长以及不能工作于非平稳环境中的问题,引入了一种由主分量分析实现自适应特征提取的在线无监督学习(LEAP)神经网络(NN)。首先将已分段的一周期DS-CDMA信号作为NN的输入信号,用NN各权值向量的符号函数代表DS-CDMA信号各用户的伪码序列,然后通过不断输入信号来反复训练权值向量直至收敛,最终DS-CDMA信号各用户的伪码序列就可以通过各权值向量的符号函数重建出来。此外,采用变步长以提高收敛速度。理论分析与仿真实验表明,LEAP NN至少可以实现-20 d B信噪比下10个用户的非等功率同步DS-CDMA伪码序列盲估计,并且比传统的Sanger NN具有更快的收敛速度。展开更多
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 ...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。展开更多
基金supported by the National Natural Science Foundation of China (10776040 60602057)+4 种基金Program for New Century Excellent Talents in University (NCET)the Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003)the Natural Science Foundation of Chongqing Science and Technology Commission (CSTC2009BB2287)the Natural Science Foundation of Chongqing Municipal Education Commission (KJ060509 KJ080517)
文摘This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.
文摘针对特征分解方法在实现非等功率同步直接序列码分多址(DS-CDMA)信号伪码序列盲估计时存在的处理数据向量不能太长以及不能工作于非平稳环境中的问题,引入了一种由主分量分析实现自适应特征提取的在线无监督学习(LEAP)神经网络(NN)。首先将已分段的一周期DS-CDMA信号作为NN的输入信号,用NN各权值向量的符号函数代表DS-CDMA信号各用户的伪码序列,然后通过不断输入信号来反复训练权值向量直至收敛,最终DS-CDMA信号各用户的伪码序列就可以通过各权值向量的符号函数重建出来。此外,采用变步长以提高收敛速度。理论分析与仿真实验表明,LEAP NN至少可以实现-20 d B信噪比下10个用户的非等功率同步DS-CDMA伪码序列盲估计,并且比传统的Sanger NN具有更快的收敛速度。
文摘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。
