OSA's Digital Library

Applied Optics

Applied Optics


  • Vol. 40, Iss. 9 — Mar. 20, 2001
  • pp: 1488–1492

Consideration of non-Poisson distributions for lidar applications

Andrew J. Gerrard, Timothy J. Kane, Jeffrey P. Thayer, Christopher S. Ruf, and Richard L. Collins  »View Author Affiliations

Applied Optics, Vol. 40, Issue 9, pp. 1488-1492 (2001)

View Full Text Article

Enhanced HTML    Acrobat PDF (255 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Poisson statistics are traditionally used to estimate the mean and standard deviation of the mean in time–range realizations of received photon counts from stationary processes in incoherent-detection lidar systems. However, this approach must be modified if the process under study is measurably nonstationary to account for any additional (and potentially unanticipated) variability. We demonstrate that the modified approach produces a different form for the estimated standard deviation of the mean for lidar return counts, which can also be applied to binning of higher-order data products. This modified technique also serves to determine optimum time–range integrations, diagnose system stability, and constrain operational modes.

© 2001 Optical Society of America

OCIS Codes
(000.4430) General : Numerical approximation and analysis
(000.5490) General : Probability theory, stochastic processes, and statistics
(280.3640) Remote sensing and sensors : Lidar

Original Manuscript: May 24, 2000
Revised Manuscript: December 5, 2000
Published: March 20, 2001

Andrew J. Gerrard, Timothy J. Kane, Jeffrey P. Thayer, Christopher S. Ruf, and Richard L. Collins, "Consideration of non-Poisson distributions for lidar applications," Appl. Opt. 40, 1488-1492 (2001)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. P. R. Bevington, D. K. Robinson, Data Reduction and Error Analysis for the Physical Sciences, 2nd ed. (McGraw-Hill, Boston, 1992).
  2. J. R. Taylor, An Introduction to Error Analysis; The Study of Uncertainties in Physical Measurements (University Science, Mill Valley, Calif., 1982).
  3. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd ed. (McGraw-Hill, New York, 1991).
  4. R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Krieger, Malabar, Fla., 1992).
  5. J. P. Thayer, N. B. Nielsen, R. E. Warren, C. J. Heinselman, J. Sohn, “Rayleigh lidar systems for middle atmosphere research in the Arctic,” Opt. Eng. 36, 2045–2061 (1997). [CrossRef]
  6. E. Durieux, L. Fiorani, “Measurement of the lidar signal fluctuation with a shot-per-shot instrument,” App. Opt. 37, 7128–7131 (1998). [CrossRef]
  7. C. S. Ruf, S. E. Beus, “Retrieval of tropospheric water vapor scale height from horizontal turbulence structure,” IEEE Trans. Geosci. Remote Sens. 35, 203–211 (1997). [CrossRef]
  8. C. S. Gardner, D. C. Senft, T. J. Beatty, R. E. Bills, C. A. Hostetler, “Rayleigh and sodium lidar techniques for measuring middle atmosphere density, temperature, and wind perturbations and their spectra,” in World Ionosphere/Thermosphere Study Handbook, S. H. Liu, B. Edwards, eds. (University of Illinois, Urbana-Champaign, Ill., 1989).
  9. C. S. Gardner, “Sodium resonance fluorescence lidar applications in atmospheric science and astronomy,” Proc. IEEE 77, 408–418 (1989). [CrossRef]

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.


Fig. 1 Fig. 2

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited