Noise analysis of lidar backscattering signal using forward and backward Kalman filtering algorithm with generalized random walk structures

Jialing Gao, Zunan Wu, Zhongliang Chen, Jianming Liang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recursive estimation of high-frequency noise in lidar backscattering signal based on forward and backward linear Kalman filtering algorithms are explored. Using state-space techniques, the lidar aerosol backscattering signal is identified following generalized random walk (GRW) structures. Comparisons of the estimation results between different Kalman-GRW filters are given in case studies. The spectral tests of the given examples show that the forward and backward Kalman filtering algorithms processing with the GRW structures are applicable low-pass filters for the smoothing of lidar data.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsJ. Wang, B. Wu, T. Ogawa, Z.H. Guan
Pages446-452
Number of pages7
Volume3501
StatePublished - 1998
Externally publishedYes
EventOptical Remote Sensing of the Atmosphere and Clouds - Beijing, China
Duration: Sep 15 1998Sep 17 1998

Other

OtherOptical Remote Sensing of the Atmosphere and Clouds
CountryChina
CityBeijing
Period9/15/989/17/98

Keywords

  • Backscattering
  • Fixed-interval smoothing
  • Forward and backward Kalman filtering
  • Generalized random walk structure
  • Inversion
  • Noise variance ratio
  • Recursive estimation
  • Signal-to-noise ratio
  • State-space approach

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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  • Cite this

    Gao, J., Wu, Z., Chen, Z., & Liang, J. (1998). Noise analysis of lidar backscattering signal using forward and backward Kalman filtering algorithm with generalized random walk structures. In J. Wang, B. Wu, T. Ogawa, & Z. H. Guan (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 3501, pp. 446-452)