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

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.