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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 35, Iss. 33 — Nov. 20, 1996
  • pp: 6466–6478

Joint estimation and identification of lidar log power returns in a switching environment

D. G. Lainiotis and Paraskevas Papaparaskeva  »View Author Affiliations


Applied Optics, Vol. 35, Issue 33, pp. 6466-6478 (1996)
http://dx.doi.org/10.1364/AO.35.006466


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Abstract

The problem of estimating the return power in a laser integrated radar (lidar) system in the presence of multiplicative noise and partially unmodeled dynamics is explored. Several nonlinear methodologies are reviewed and compared to develop a systematic approach to signal model identification and estimation. The situations considered operate in mode-switching environments, that is, the desired unknown parameters are allowed to vary according to sudden jumps exhibiting discontinuous behavior at random times. Partitioning-based, parallel-structured techniques are shown to be significantly superior to the usual extended Kalman filter algorithm.

© 1996 Optical Society of America

History
Original Manuscript: June 15, 1995
Revised Manuscript: May 8, 1996
Published: November 20, 1996

Citation
D. G. Lainiotis and Paraskevas Papaparaskeva, "Joint estimation and identification of lidar log power returns in a switching environment," Appl. Opt. 35, 6466-6478 (1996)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-35-33-6466


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References

  1. B. J. Rye, “Power ratio estimation in incoherent backscatter LIDAR: heterodyne receiver with square law detection,” J. Climate Appl. Meteorol. 22, 1899–1913 (1983). [CrossRef]
  2. B. J. Rye, R. M. Hardesty, “Time series identification and Kalman filtering techniques for doppler LIDAR velocity estimation,” Appl. Opt. 28, 879–891 (1989). [CrossRef] [PubMed]
  3. B. J. Rye, R. M. Hardesty, “Nonlinear Kalman filtering techniques for incoherent backscatter LIDAR: return power and log power estimation,” Appl. Opt. 28, 3908–3917 (1989). [CrossRef] [PubMed]
  4. B. J. Rye, “A wavelength switching algorithm for single laser differential absorption LIDAR systems,” in Laser Applications in Meteorology and Earth Atmospheric Remote Sensing, M. M. Sokoloski, ed., Proc. SPIE 1062, 267–273 (1989).
  5. B. J. Rye, “Kalman filtering in LIDAR,” in Proceedings of the Fifth Conference on Coherent Laser Radar, Munich, 1989.
  6. B. J. Rye, R. M. Hardesty, “Power estimator bias in filtered incoherent backscatter heterodyne LIDAR returns,” in Coherent Laser Radar, 1991 OSA Technical Digest Series (Optical Society of America, Washington, D.C., 1991), Poster paper.
  7. W. B. Grant, A. M. Brothers, J. R. Bogan, “DIAL signal averaging,” Appl. Opt. 27, 1934–1938 (1988). [CrossRef] [PubMed]
  8. M. J. T. Milton, P. T. Woods, “Pulse averaging methods for a laser remote monitoring system using atmospheric backscatter,” Appl. Opt. 26, 2598–2603 (1987). [CrossRef] [PubMed]
  9. D. S. Zrnic, “Mean power estimation with a recursive filter,” IEEE Trans. Aerosp. Electron. Syst. AES-13, 281–289 (1977). [CrossRef]
  10. N. Menyuk, D. K. Killinger, C. R. Menyuk, “Limitations of signal averaging due to temporal correlation in laser remote sensing measurements,” Appl. Opt. 21, 3377–3383 (1982). [CrossRef] [PubMed]
  11. R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82, 35–45 (1960). [CrossRef]
  12. J. V. Candy, Signal Processing: The Model-Based Approach (McGraw-Hill, New York, 1986).
  13. A. Gelb, ed., Applied Optimal Estimation (MIT, Cambridge, 1974).
  14. R. E. Warren, “Adaptive Kalman–Bucy filter for differential absorption LIDAR time series data,” Appl. Opt. 26, 4755–4760 (1987). [CrossRef] [PubMed]
  15. J. A. Nelder, R. Mead, “A simplex method for function minimization,” Comput. J. 7, 308–313 (1965).
  16. D. Letalick, M. Millnert, I. Renhorn, “Terrain segmentation using laser radar range data,” Appl. Opt. 31, 2883–2890 (1992). [CrossRef] [PubMed]
  17. G. A. Ackerson, K. S. Fu, “On state estimation in switching environments,” IEEE Trans. Autom. Control AC-15, 10–17 (1970). [CrossRef]
  18. L. S. Segal, D. G. Lainiotis, “Partitioned adaptive estimation of time-varying random parameters with applications to economic forecasting,” in Proceedings of the Joint Automatic Control Conference, Denver, Colo. (1979), pp. 527–531.
  19. D. E. Gustafson, A. S. Willsky, J. Y. Wang, M. C. Lancaster, J. H. Triebwasser, “ECG/VCG rhythm diagnosis using statistical signal analysis—I. Identification of persistent rhythms,” IEEE Trans. Biomed. Eng. BME-25, 344–353 (1978). [CrossRef]
  20. D. E. Gustafson, A. S. Willsky, J. Y. Wang, M. C. Lancaster, J. H. Triebwasser, “ECG/VCG rhythm diagnosis using statistical signal analysis—II. Identification of transient rhythms,” IEEE Trans. Biomed. Eng. BME-25, 353–361 (1978). [CrossRef]
  21. H. Akashi, H. Kumamoto, “Random sampling approach to state estimation in switching environments,” Automatica 13, 429–434 (1977). [CrossRef]
  22. J. K. Tugnait, A. H. Haddad, “A detection estimation scheme for state estimation in switching environments,” Automatica 15, 477–481 (1979). [CrossRef]
  23. B. Jeyendran, V. U. Reddy, “Recursive system identification in the presence of burst disturbance,” Signal Process. 20, 227–245 (1990). [CrossRef]
  24. B. S. Rao, H. F. Durrant-Whyte, “A decentralized Bayesian algorithm for identification of tracked targets,” IEEE Trans. Syst. Man Cybern. 23, 1683–1698 (1993). [CrossRef]
  25. J. K. Tugnait, “Detection and estimation for abruptly changing systems,” Automatica 18, 607–615 (1982). [CrossRef]
  26. D. G. Lainiotis, “Sequential structure and parameter adaptive pattern recognition, part I: supervised learning,” IEEE Trans. Inf. Theory IT-16, 548–556 (1970). [CrossRef]
  27. D. G. Lainiotis, “Joint detection, estimation, and system identification,” Inf. Control J. 19, 75–92 (1971). [CrossRef]
  28. D. G. Lainiotis, “Adaptive pattern recognition: a state variable approach,” in Advances in Pattern Recognition, M. Watanabe, ed. (Academic, New York, 1972).
  29. D. G. Lainiotis, S. K. Park, “On joint detection, estimation and system identification: discrete data case,” Int. J. Control 17, 609–633 (1973). [CrossRef]
  30. D. Andrisani, F. P. Kuhl, D. Gleason, “A nonlinear tracker using attitude measurements,” IEEE Trans. Aerosp. Electron. Syst. AES-22, 533–539 (1986). [CrossRef]
  31. D. Andrisani, E. T. Kim, J. Schierman, “A nonlinear helicopter tracker using attitude measurements,” IEEE Trans. Aerosp. Electron. Syst. 27, 40–47 (1991). [CrossRef]
  32. H. W. Sorenson, A. R. Stubberud, “Nonlinear filtering by approximation of the A-posteriori density,” Int. J. Control 18, 33–51 (1968). [CrossRef]
  33. H. W. Sorenson, D. L. Alspach, “Recursive Bayesian estimation using Gaussian sums,” Automatica 7, 465–479 (1971). [CrossRef]
  34. D. G. Lainiotis, “Optimal adaptive estimation: structure and parameter adaptation,” IEEE Trans. Autom. Control AC-16, 160–170 (1971). [CrossRef]
  35. D. G. Lainiotis, “Optimal nonlinear estimation,” Int. J. Control 14, 1137–1148 (1971). [CrossRef]
  36. D. G. Lainiotis, “Partitioned estimation algorithms II: nonlinear estimation,” J. Inf. Sci. 7, 202–235 (1974).
  37. D. G. Lainiotis, “Partitioning: a unifying framework for adaptive systems, I: estimation,” Proc. IEEE 64, 1126–1143 (1976). [CrossRef]

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