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A modified eigen-structure analyzer to lower SNR DS-SS signals under narrow band interferences

机译:改进的本征结构分析仪,可在窄带干扰下降低SNR DS-SS信号

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This paper proposes a modified approach to eigen-structure analysis of direct sequence spread spectrum (DS-SS) signals under narrow-band interferences (NBIs), which can estimate the pseudo noise (PN) sequence blindly in lower signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR) DS-SS signals. Of course, some parameters of DS-SS signals (such as the period and chip interval of PN sequences) need to be known. The received signal is divided into continuous non-overlapping temporal vectors according to two periods (not the one period before in LT. Zhang, A. Mu, C. Zhang, Analyze the eigen-structure of DS-SS signals under narrow band interferences, Digital Signal Process. 16 (6) (2006) 746-753]) of PN sequence, after which their correlation matrix is calculated and accumulated vector by vector. An eigenvalue decomposition operation can be applied to the matrices to analyze blindly the eigen-structure (including the NBI eigen-structures and PN sequences) of received signals from the principal component eigenvectors. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by one principal eigenvector (not the two component eigenvectors before in LT. Zhang, A. Mu, C. Zhang, Analyze the eigen-structure of DS-SS signals under narrow band interferences, Digital Signal Process. 16 (6) (2006) 746-753]) only. Theoretic analysis and experimental results show that the approach is more effective than the method before in LT. Zhang, A. Mu, C. Zhang, Analyze the eigen-structure of DS-SS signals under narrow band interferences, Digital Signal Process. 16 (6) (2006) 746-753]. It can also work well in the lower SNR and SIR environments. (C) 2007 Elsevier Inc. All rights reserved.
机译:本文提出了一种在窄带干扰(NBI)下直接序列扩频(DS-SS)信号本征结构分析的改进方法,该方法可以在较低的信噪比下盲目估计伪噪声(PN)序列。 (SNR)和信噪比(SIR)DS-SS信号。当然,需要知道DS-SS信号的某些参数(例如PN序列的周期和码片间隔)。接收到的信号根据两个周期(而不是LT中的一个周期)被分为连续的非重叠时间矢量,在窄带干扰下,分析DS-SS信号的本征结构, PN序列的数字信号处理(Digital Signal Process。16(6)(2006)746-753]),然后计算它们的相关矩阵并逐个向量地累加。可以对矩阵进行特征值分解操作,以盲法分析来自主成分特征向量的接收信号的特征结构(包括NBI特征结构和PN序列)。由于时间窗的持续时间是PN序列的两个周期,因此PN序列可以通过一个主要特征向量(不是LT中以前的两个分量特征向量)进行重构.Zhang,A.Mu,C.Zhang,分析DS的特征结构-SS信号在窄带干扰下,仅适用于Digital Signal Process。16(6)(2006)746-753])。理论分析和实验结果表明,该方法比LT中的方法更有效。 Zhang,A。Mu,C。Zhang,在窄带干扰下分析DS-SS信号的本征结构,数字信号处理。 16(6)(2006)746-753]。它也可以在较低的SNR和SIR环境中很好地工作。 (C)2007 Elsevier Inc.保留所有权利。

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