### 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 language | English (US) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |

Editors | J. Wang, B. Wu, T. Ogawa, Z.H. Guan |

Pages | 446-452 |

Number of pages | 7 |

Volume | 3501 |

State | Published - 1998 |

Externally published | Yes |

Event | Optical Remote Sensing of the Atmosphere and Clouds - Beijing, China Duration: Sep 15 1998 → Sep 17 1998 |

### Other

Other | Optical Remote Sensing of the Atmosphere and Clouds |
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Country | China |

City | Beijing |

Period | 9/15/98 → 9/17/98 |

### 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)