OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 44, Iss. 6 — Feb. 20, 2005
  • pp: 1077–1083

Antinoise approximation of the lidar signal with wavelet neural networks

Hai-Tao Fang, De-Shuang Huang, and Yong-Hua Wu  »View Author Affiliations


Applied Optics, Vol. 44, Issue 6, pp. 1077-1083 (2005)
http://dx.doi.org/10.1364/AO.44.001077


View Full Text Article

Enhanced HTML    Acrobat PDF (216 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We propose a new, to our knowledge, denoising method for lidar signals based on a regression model and a wavelet neural network (WNN) that permits the regression model not only to have a good wavelet approximation property but also to make a neural network that has a self-learning and adaptive capability for increasing the quality of lidar signals. Specifically, we investigate the performance of the WNN for antinoise approximation of lidar signals by simultaneously addressing simulated and real lidar signals. To clarify the antinoise approximation capability of the WNN for lidar signals, we calculate the atmosphere temperature profile with the real signal processed by the WNN. To show the contrast, we also demonstrate the results of the Monte Carlo moving average method and the finite impulse response filter. Finally, the experimental results show that our proposed approach is significantly superior to the traditional methods.

© 2005 Optical Society of America

OCIS Codes
(010.0010) Atmospheric and oceanic optics : Atmospheric and oceanic optics
(010.1290) Atmospheric and oceanic optics : Atmospheric optics
(010.3640) Atmospheric and oceanic optics : Lidar

History
Original Manuscript: June 11, 2004
Revised Manuscript: September 29, 2004
Manuscript Accepted: October 13, 2004
Published: February 20, 2005

Citation
Hai-Tao Fang, De-Shuang Huang, and Yong-Hua Wu, "Antinoise approximation of the lidar signal with wavelet neural networks," Appl. Opt. 44, 1077-1083 (2005)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-44-6-1077


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. P. S. Argall, R. J. Sica, LIDAR in the Encyclopedia of Atmospheric Sciences (Academic, London, 2002).
  2. S. Lerkvarnyu, “Moving average method for time series lidar data,” http://www.gisdevelopment.net/aars/acrs/1998/ps3/ps3016.shtml .
  3. S. Amoruso, A. Amodeo, M. Armenante, A. Boselli, “Development of a tunable IR lidar system,” Opt. Lasers Eng. 37, 521–532 (2002). [CrossRef]
  4. K. Stelmaszczyk, “New method of elaboration of the lidar signal,” Appl. Phys. B 70, 295–299 (2000). [CrossRef]
  5. C. Andrien, “Robust full Bayesian learning for radial basis networks,” Neural Comput. 13, 2359–2407 (2001). [CrossRef]
  6. A. B. Utkin, “Detection of small forest fires by lidar,” Appl. Phys. B 74, 77–83 (2002). [CrossRef]
  7. V. Kreinovich, “Wavelet neural network are asymptotically optimal approximators for function of one variable,” in IEEE World Congress on Computational Intelligence, 1994 IEEE International Conference (IEEE, Piscataway, N.J., 1994), Vol. 1, pp. 299–304.
  8. D. S. Huang, Systematic Theory of Neural Networks for Pattern Recognition (Publishing House of Electronic Industry of China, Beijing, 1996).
  9. F. G. Fernald, “Analysis of atmospheric lidar observations: some comments,” Appl. Opt. 23, 652–653 (1984). [CrossRef] [PubMed]
  10. D. S. Huang, Y. Q. Han, “A detection method of high resolution radar targets based on position correlation,” J. Electron. 5, 107–115 (1998).
  11. C. F. Bas, “The layered perceptron versus the Neyman–Pearson optimal detection,” in Proceedings of the International Joint Conference on Neural Networks (IEEE, Piscataway, N.J., 1992), pp. 1486–1489.
  12. D. S. Huang, Z. Bao, “The study of recognition technique of radar targets one dimensional images based on radial basis function network,” J. Electron. 12, 200–210 (1995).
  13. D. S. Huang, Intelligent Signal Processing Technique for High Resolution Radars (Publishing House of Machine Industry of China, Beijing, 2001).
  14. I. Daubechise, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Trans. Inf. Theory 36, 961–1005 (1990). [CrossRef]
  15. J. Zhang, “Wavelet neural networks for function learning,” IEEE Trans. Signal Process. 43, 1485–1497 (1995). [CrossRef]
  16. L. Cao, Y. Hong, “Predicting chaotic time series with wavelet networks,” Physica D 85, 225–238 (1995). [CrossRef]
  17. See http://www.atmosphere.mpg.de/enid/791 .
  18. A. Hauchecorne, M. L. Chanin, “Density and temperature profiles obtained by lidar between 35 and 75 km,” Geophys. Res. Lett. 7, 565–568 (1980). [CrossRef]
  19. T. Shibata, M. Kobuchi, M. Maeda, “Measurements of density and temperature profiles in the middle atmosphere with XeF lidar,” Appl. Opt. 25, 685–688 (1986). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited