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Reconstruction of backscatter and extinction coefficients in lidar: a stochastic filtering approach

Reconstruction of backscatter and extinction coefficients in lidar: a stochastic filtering approach,10.1109/TGRS.2003.817216,IEEE Transactions on Geos

Reconstruction of backscatter and extinction coefficients in lidar: a stochastic filtering approach   (Citations: 1)
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Reconstruction of the backscatter and extinction coefficients is a crucial step in many quantitative remote sensing applications, such as radar, light detection and ranging (lidar), and sonar. We present a novel stochastic filtering approach for the estimation of the backscatter and extinction coefficients from time-range elastic-backscatter lidar data. The Bayesian perspective is adopted; we take as prior a causal first-order autoregressive Gauss-Markov random field tailored to enforce smoothness on time and range dimensions. By using a reduced-order state-space representation of the prior, we derive a suboptimal stochastic filter that recursively computes the backscatter and extinction coefficients at each range-time inversion cell. The estimator is a kind of adaptive extended Kalman filter, being efficient from the computational point of view. A set of experiments illustrates the effectiveness of the proposed approach, namely its advantage over the classical Klett deterministic approach.
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    • ...Solutions for calculating backscatter and extinction have included the slope method [1], the ratio-constraint methods, including those by Klett [2], [3] and Fernald [4], and extended Kalman filter (EKF) methods [5]‐[7]...
    • ...Different versions of the EKF have also been applied to the lidar inversion problem by Rocadenbosch et al. [5], Warren et al. [6], and Dias et al. [7]...

    Christian C. Marchantet al. An Iterative Least Square Approach to Elastic-Lidar Retrievals for Wel...

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