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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 39, Iss. 5 — Feb. 10, 2000
  • pp: 850–859

Statistical-uncertainty-based adaptive filtering of lidar signals

P. L. Fuehrer, C. A. Friehe, T. S. Hristov, D. I. Cooper, and W. E. Eichinger  »View Author Affiliations


Applied Optics, Vol. 39, Issue 5, pp. 850-859 (2000)
http://dx.doi.org/10.1364/AO.39.000850


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Abstract

An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H2O and the N2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem.

© 2000 Optical Society of America

OCIS Codes
(070.0070) Fourier optics and signal processing : Fourier optics and signal processing
(070.6110) Fourier optics and signal processing : Spatial filtering
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(280.3640) Remote sensing and sensors : Lidar

History
Original Manuscript: April 9, 1999
Revised Manuscript: October 25, 1999
Published: February 10, 2000

Citation
P. L. Fuehrer, C. A. Friehe, T. S. Hristov, D. I. Cooper, and W. E. Eichinger, "Statistical-uncertainty-based adaptive filtering of lidar signals," Appl. Opt. 39, 850-859 (2000)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-39-5-850


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References

  1. D. I. Cooper, W. E. Eichinger, D. B. Holtkamp, R. R. Karl, C. R. Quick, W. Dugas, L. Hipps, “Spatial variability of water vapor turbulent transfer within the boundary layer,” Boundary-Layer Meterol. 61, 389–405 (1992). [CrossRef]
  2. D. I. Cooper, W. E. Eichinger, S. Barr, W. Cottingame, M. V. Hynes, C. F. Keller, C. F. Lebeda, D. A. Poling, “High resolution properties of the equatorial Pacific marine atmospheric boundary layer from lidar and radiosonde observations,” J. Atmos. Sci. 53, 2054–2075 (1996). [CrossRef]
  3. W. E. Eichinger, D. I. Cooper, M. Parlange, G. Katul, “The application of a scanning, water Raman-lidar as a probe of the atmospheric boundary layer,” IEEE Trans. Geosci. Remote Sens. 31, 70–79 (1993). [CrossRef]
  4. W. E. Eichinger, D. I. Cooper, F. L. Archuletta, D. Hof, D. B. Holtkamp, R. R. Karl, C. R. Quick, J. Tiee, “Development of a scanning, solar-blind, water Raman lidar,” Appl. Opt. 33, 3923–3932 (1994). [CrossRef] [PubMed]
  5. 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]
  6. D. G. Lainiotis, P. Papaparaskeva, G. Kothapalli, K. Plataniotis, “Adaptive filter applications to lidar: return power and log power estimation,” IEEE Trans. Geosci. Remote Sens. 34, 886–891 (1996). [CrossRef]
  7. R. E. Warren, “Concentration estimation from differential absorption lidar using nonstationary Wiener filtering,” Appl. Opt. 28, 5047–5051 (1989). [CrossRef] [PubMed]

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