### 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) |
---|---|

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 |

### Fingerprint

### 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

### Cite this

*Proceedings of SPIE - The International Society for Optical Engineering*(Vol. 3501, pp. 446-452)

**Noise analysis of lidar backscattering signal using forward and backward Kalman filtering algorithm with generalized random walk structures.** / Gao, Jialing; Wu, Zunan; Chen, Zhongliang; Liang, Jianming.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of SPIE - The International Society for Optical Engineering.*vol. 3501, pp. 446-452, Optical Remote Sensing of the Atmosphere and Clouds, Beijing, China, 9/15/98.

}

TY - GEN

T1 - Noise analysis of lidar backscattering signal using forward and backward Kalman filtering algorithm with generalized random walk structures

AU - Gao, Jialing

AU - Wu, Zunan

AU - Chen, Zhongliang

AU - Liang, Jianming

PY - 1998

Y1 - 1998

N2 - 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.

AB - 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.

KW - Backscattering

KW - Fixed-interval smoothing

KW - Forward and backward Kalman filtering

KW - Generalized random walk structure

KW - Inversion

KW - Noise variance ratio

KW - Recursive estimation

KW - Signal-to-noise ratio

KW - State-space approach

UR - http://www.scopus.com/inward/record.url?scp=0032404840&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032404840&partnerID=8YFLogxK

M3 - Conference contribution

VL - 3501

SP - 446

EP - 452

BT - Proceedings of SPIE - The International Society for Optical Engineering

A2 - Wang, J.

A2 - Wu, B.

A2 - Ogawa, T.

A2 - Guan, Z.H.

ER -