摘要
We develop a regularization-based algorithm for reconstructing the Cn^2 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 HV5∕7model 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 Cn^2 profile from DCIM lidar.
We develop a regularization-based algorithm for reconstructing the Cn^2 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 HV5∕7model 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 Cn^2 profile from DCIM lidar.
基金
supported by the National Natural Science Foundation of China under Grant No.41405014
