Authors:
Tianqi Zhang
1
;
Shaosheng Dai
1
;
Zhengzhong Zhou
2
and
Xiaokang Lin
3
Affiliations:
1
InstituteSchool of Communication and Information Engineering / Institute of Signal Processing and System On Chip , (ISPSOC), Chongqing University of Posts and Telecommunications (CQUPT), China
;
2
School of Communication and Information Engineering, University of Electronic Science and Technology of China (UESTC), China
;
3
Graduate School at Shenzhen of Tsinghua University, China
Keyword(s):
Generalized Hebbian algorithm (GHA), neural network (NN), direct sequence spread spectrum (DS-SS) signals, pseudo noise (PN) sequence.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Signal Reconstruction
Abstract:
This paper proposes a modified Sanger’s generalized Hebbian algorithm (GHA) neural network (NN) method to estimate and track the pseudo noise (PN) sequence in lower signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS) signals. The proposed method is based on eigen-analysis of DS-SS signals. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. The PN sequence can be estimated and tracked by the principal eigenvector of autocorrelation matrix in the end. But the eigen-analysis method becomes inefficiency when the estimated PN sequence becomes longer or the estimated PN sequence becomes time varying. In order to overcome these shortcomings, we use a modified Sanger’s GHA NN to realize the PN sequence estimation and tracking from lower SNR input DS-SS signals adaptively
and effectively.
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