• Banta, R. M., L. D. Olivier, and D. H. Levinson, 1993: Evolution of the Monterey Bay sea-breeze layer as observed by pulsed Doppler lidar. J. Atmos. Sci.,50, 3959–3982.

    • Crossref
    • Export Citation
  • Dabas, A., J. L. Zarader, and P. H. Flamant, 1996: Decision algorithm for the ALADIN Signal Processor. ESA Contract Final Report, 100 pp.

  • Delville, P., C. Loth, P. H. Flamant, D. Bruneau, T. Le Floch, and J. C. Farcy, 1995: A new Te–CO2 laser for coherent lidar and wind applications. Proc. Eighth Coherent Laser Radar Conf., Keystone, CO, 297–300.

  • Drobinski, P., and Coauthors, 1997: Observations of a suburban atmospheric boundary layer evolution by LMD pulsed infrared Doppler lidar and other sensors during ECLAP experiment. Proc. Ninth Conf. on Coherent Laser Radar, Linköping, Sweden, 252–255.

  • Frehlich, R. G., 1993: Cramer-Rao bound for Gaussian random processes and applications to radar processing of atmospheric signals. IEEE Trans. Geosci. Remote Sens.,31, 1123–1131.

    • Crossref
    • Export Citation
  • ——, and M. J. Yadlowsky, 1994: Performance of mean-frequency estimators for Doppler radar and lidar. J. Atmos. Oceanic Technol.,11, 1217–1230.

    • Crossref
    • Export Citation
  • ——, S. M. Hannon, and S. W. Henderson, 1994: Performance of a 2-μm coherent Doppler lidar for wind measurements. J. Atmos. Oceanic Technol.,11, 1517–1528.

    • Crossref
    • Export Citation
  • Goodman, J. W., 1975: Statistical properties of laser speckles patterns. Laser Speckles and Related Phenomena, J. C. Dainty, Ed., Vol. 9, Springer Topics in Applied Physics, Springer-Verlag, 51–58.

    • Crossref
    • Export Citation
  • Hardesty, R. M., 1986: Performance of discrete spectral peak frequency estimator for Doppler wind velocity measurements. IEEE Trans. Geosci. Remote Sens.,24, 777–783.

    • Crossref
    • Export Citation
  • Intrieri, J. M., A. J. Bedard Jr., and R. M. Hardesty, 1990: Details of colliding thunderstorm outflows as observed by Doppler lidar. J. Atmos. Sci.,47, 1081–1098.

    • Crossref
    • Export Citation
  • Lee, R. W., 1978: Performance of the Poly-Pulse-Pair Doppler estimator. Lassen Research Memo. 78-03.

  • Levin, M. J., 1968: Power spectrum parameter estimation. IEEE Trans. Inf. Theory,11, 100–107.

    • Crossref
    • Export Citation
  • May, P. T., and R. G. Strauch, 1989: An examination of wind profiler signal processing algorithms. J. Atmos. Oceanic Technol.,6, 731–735.

    • Crossref
    • Export Citation
  • Menut, L., C. Flamant, J. Pelon, R. Valentin, P. H. Flamant, E. Dupont, and B. Carissimo, 1996: Study of the boundary layer structure over the Paris agglomeration as observerd during the ECLAP Experiment. Advances in Atmospheric Remote Sensing with Lidar, A. Ansmann et al., Eds., Springer, 15–18.

    • Crossref
    • Export Citation
  • Miller, K. S., and M. M. Rochwarger, 1972: A covariance approach to spectral moment estimation. IEEE Trans. Inf. Theory,18, 588–596.

    • Crossref
    • Export Citation
  • Nehoraï, A., 1985: A minimal parameter adaptive notch filter with constrained poles and zeros. IEEE Trans. Acoust., Speech, Signal Process.,33, 983–996.

    • Crossref
    • Export Citation
  • ——, and D. Starer, 1990: Adaptive pole estimation. IEEE Trans. Accoust., Speech, Signal Process.,38, 825–838.

    • Crossref
    • Export Citation
  • Post, M. J., and W. D. Neff, 1986: Doppler lidar measurements of winds in a narrow mountain valley. Bull. Amer. Meteor. Soc.,67, 274–281.

    • Crossref
    • Export Citation
  • Rye, B. J., 1995: Return power estimation for targets spread in range. Coherent Laser Radar, 1995 OSA Technical Digest Series, Vol. 19, Optical Society of America, 202–205.

  • ——, and R. M. Hardesty, 1993: Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I. Spectral accumulation and the Cramer-Rao lower bound. IEEE Trans. Geosci. Remote Sens.,31, 16–27.

    • Crossref
    • Export Citation
  • ——, and ——, 1997: Detection techniques for validating Doppler estimates in heterodyne lidar. Appl. Opt.,36, 1940–1951.

    • Crossref
    • Export Citation
  • Zarader, J. L., G. Ancellet, A. Dabas, N. K. M’Sirdi, and P. H. Flamant, 1996: Performance of an adaptative notch filter for spectral analysis of coherent lidar signals. J. Atmos. Oceanic Technol.,13, 16–28.

    • Crossref
    • Export Citation
  • Zrnic, D. S., 1975: Simulation of weatherlike Doppler spectra and signals. J. Appl. Meteor.,14, 619–620.

    • Crossref
    • Export Citation
  • ——, 1979: Estimation of spectral moments for weather echoes. IEEE Trans. Geosci. Remote Sens.,17, 113–127.

    • Crossref
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 104 104 77
PDF Downloads 13 13 4

Adaptive Filters for Frequency Estimate of Heterodyne Doppler Lidar Returns: Recursive Implementation and Quality Control

View More View Less
  • 1 Météo-France, Centre National de Recherches Météorologiques, Toulouse, France
  • | 2 Centre National de la Recherche Scientifique, Laboratoire de Métérologie Dynamique, Ecole Polytechnique, Palaiseau, France
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Unreliable frequency estimates at low signal-to-noise ratios, provided by a heterodyne Doppler lidar (HDL), undermines any data analysis scheme requiring high-accuracy wind fields retrievals. To meet demanding specifications, that is, high accuracy associated with high reliability on radial velocity components, a quality control (QC) procedure has to be implemented at signal processing level. The authors propose use of a recursive implementation of an adaptive filter for frequency estimate coupled with a QC that combines a statistical test on the signal energy filtered out as proposed by Rye and Hardesty and a persistency criterion (PC). The PC leads to improved performance with respect to the percentage of data finally accepted and frequency accuracy. The performance is validated using simulated signals and HDL actual data.

* Current affiliation: U.S. Department of Commerce, NOAA, Boulder, Colorado.

Corresponding author address: Dr. Philippe Drobinski, Laboratoire de Meteorologie Dynamique, Ecole Polytechnique, 91128 Palaiseau Cedex, France.

Email: drobi@stathp.polytechnique.fr

Abstract

Unreliable frequency estimates at low signal-to-noise ratios, provided by a heterodyne Doppler lidar (HDL), undermines any data analysis scheme requiring high-accuracy wind fields retrievals. To meet demanding specifications, that is, high accuracy associated with high reliability on radial velocity components, a quality control (QC) procedure has to be implemented at signal processing level. The authors propose use of a recursive implementation of an adaptive filter for frequency estimate coupled with a QC that combines a statistical test on the signal energy filtered out as proposed by Rye and Hardesty and a persistency criterion (PC). The PC leads to improved performance with respect to the percentage of data finally accepted and frequency accuracy. The performance is validated using simulated signals and HDL actual data.

* Current affiliation: U.S. Department of Commerce, NOAA, Boulder, Colorado.

Corresponding author address: Dr. Philippe Drobinski, Laboratoire de Meteorologie Dynamique, Ecole Polytechnique, 91128 Palaiseau Cedex, France.

Email: drobi@stathp.polytechnique.fr

Save