USE A NEURAL NETWORKS TO ESTIMATE AND TRACK THE PN SEQUENCE IN LOWER SNR DS-SS SIGNALS

Tianqi Zhang, Shaosheng Dai, Zhengzhong Zhou, Xiaokang Lin

2007

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.

References

  1. M. K. Simon, J. K. Omura, R. A. Scholtz, and B. K. Levitt, Spread Spectrum Communications Handbook. New York: McGraw-Hill, 1994.
  2. C. A. French and W. A. Gardner, “Spread spectrum despreading without the code,” IEEE Trans. Cornmun., vol. COM-34, pp. 404-408, Apr. 1986.
  3. Tianqi Zhang, Xiaokang Lin and Zhengzhong Zhou, “Blind Estimation of the PN Sequence in Lower SNR DS/SS Signals , ” IEICE Transaction On Communications, Vol.E88-B, No.7, JULY, 2005, pp. 3087-3089.
  4. Simic, S. and Zejak, A., “Blind Estimation of the Code Sequence in Spread Spectrum Radar,” the 7th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services, IEEE-TELSIKS, 2005. Vol.2, 28-30 Sept. 2005, pp: 485 - 490.
  5. Zhan, Y., Cao Z., and Lu J., “Spread-spectrum sequence estimation for DSSS signal in non-cooperative communication systems,” IEE Proc.-Commun, Vol.152, No.4, 2005, pp.476-480.
  6. T.D. Sanger, “Optimal unsupervised learning in a singlelayer linear feedforward neural networks,” Neural Networks, vol.3, pp.459-473, 1989.
  7. S. Haykin, Neural Networks-A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1999.
  8. T.W. Anderson. Asymptotic theory for principal component analysis. Ann. Math. Statist., 1963, 35: 1296-1303.
Download


Paper Citation


in Harvard Style

Zhang T., Dai S., Zhou Z. and Lin X. (2007). USE A NEURAL NETWORKS TO ESTIMATE AND TRACK THE PN SEQUENCE IN LOWER SNR DS-SS SIGNALS . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-972-8865-84-9, pages 379-384. DOI: 10.5220/0001647003790384


in Bibtex Style

@conference{icinco07,
author={Tianqi Zhang and Shaosheng Dai and Zhengzhong Zhou and Xiaokang Lin},
title={USE A NEURAL NETWORKS TO ESTIMATE AND TRACK THE PN SEQUENCE IN LOWER SNR DS-SS SIGNALS},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2007},
pages={379-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001647003790384},
isbn={978-972-8865-84-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - USE A NEURAL NETWORKS TO ESTIMATE AND TRACK THE PN SEQUENCE IN LOWER SNR DS-SS SIGNALS
SN - 978-972-8865-84-9
AU - Zhang T.
AU - Dai S.
AU - Zhou Z.
AU - Lin X.
PY - 2007
SP - 379
EP - 384
DO - 10.5220/0001647003790384