论文部分内容阅读
We develop a regularization-based algorithm for reconstructing the C_n~2 profile using the profile of Fried’s transverse coherent length(r_0) 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 C_n~2 profile from DCIM lidar.
We develop a regularization-based algorithm for reconstructing the C_n ~ 2 profile using the profile of Fried’s transverse coherent length (r_0) 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 / 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_n ~ 2 profile from DCIM lidar.