Abstract

We develop a regularization-based algorithm for reconstructing the C_n² profile using the profile of Fried’s transverse coherent length (r₀) of differential column image motion (DCIM) lidar. This algorithm consists of fitting the set of measured data to a spline function and a two-stage inversion method based on regularized least squares QR-factorization (LSQR) in combination with an adaptive selection method. The performance of this algorithm is analyzed by a simulated profile generated from the HV_5/7 model and experimental DCIM lidar data. Both the simulation and experiment support the presented approach. It is shown that the algorithm can be applied to estimate a reliable C_n² profile from DCIM lidar.

© 2016 Chinese Laser Press

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