NDLI: Parameter Estimation of DSSS Signals with Modified Fourth-Order Cumulants

Content Provider	IEEE Xplore Digital Library
Author	Xiao-Ming Zhang Zhong-Zhao Zhang
Copyright Year	2006
Description	Author affiliation: Commun. Res. Center, Harbin Inst. of Technol. (Xiao-Ming Zhang; Zhong-Zhao Zhang)
Abstract	An asymptotic estimating technique for direct sequence spread spectrum signals using modified fourth-order statistics is considered. The general higher-order cumulants may not be easily applied in signal processing with too complex computation. Based on the fourth-order cumulants with 1D slices and adaptive linear filters, an efficient algorithm is proposed to solve this problem. In order to achieve accurate frequency estimation, the first step uses the modified fourth-order cumulants to reduce the computing complexity. While the second step employs an adaptive recursive system. The fourth-order cumulants is a function of the frequency parameters and is obtained in an iterative manner. For the case of received signals with sufficient large data samples, the estimator provides good performance in frequency estimation and the white Gaussian noise suppression. Computer simulations are included to corroborate the theoretical development with different signal-to-noise ratio conditions and iterative coefficients
Sponsorship	IEEE ICIC Int. National Natural Sci. Found. of China Beijing Jiaotong Univ. Kaosiung Univ. of Appl. Sci
Starting Page	126
Ending Page	129
File Size	173778
Page Count	4
File Format	PDF
ISBN	0769526160
DOI	10.1109/ICICIC.2006.133
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Adaptive systems Gaussian noise Signal processing algorithms Adaptive filters Spread spectrum communication Nonlinear filters Adaptive signal processing Frequency estimation Higher order statistics
Content Type	Text
Resource Type	Article

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