Open AccessAnti-noise algorithm of lidar data retrieval by combining the ensemble Kalman filter and the Fernald method
Author(s) -
Feiyue Mao,
Wei Gong,
Chen Li
Publication year - 2013
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.21.008286
Subject(s) - lidar , kalman filter , algorithm , remote sensing , computer science , backscatter (email) , range (aeronautics) , noise (video) , measure (data warehouse) , filter (signal processing) , artificial intelligence , data mining , image (mathematics) , computer vision , geology , telecommunications , materials science , composite material , wireless
The lidar signal-to-noise ratio decreases rapidly with an increase in range, which severely affects the retrieval accuracy and the effective measure range of a lidar based on the Fernald method. To avoid this issue, an alternative approach is proposed to simultaneously retrieve lidar data accurately and obtain a de-noised signal as a by-product by combining the ensemble Kalman filter and the Fernald method. The dynamical model of the new algorithm is generated according to the lidar equation to forecast backscatter coefficients. In this paper, we use the ensemble sizes as 60 and the factor δ(1/2) as 1.2 after being weighed against the accuracy and the time cost based on the performance function we define. The retrieval and de-noising results of both simulated and real signals demonstrate that our method is practical and effective. An extensive application of our method can be useful for the long-term determining of the aerosol optical properties.