Turbulence profile is an important parameter for characterization of atmospheric turbulence intensity at different altitudes. Based on generalized Hufnagel-Valley atmospheric turbulence model and measurement data of DCIM lidar, we first analyzed the difficulties encountered by analytical methods, then a numerical method based on particle swarm optimization algorithm was proposed to retrieval the unknown parameters of generalized Hufnagel-Valley model. To enhance the accuracy of high altitudes, whole layer isoplanatic angle was also as a constraint in the inversion. Moreover,we compared the particle swarm optimization algorithm and Levenberg-Marquardt algorithm in term of inversion accuracy.
In this paper, the Faster R-CNN algorithm and YOLOv3 algorithm are researched and practiced based on the remote sensing image data sets. Using the same data sets and hardware environment, it mainly evaluates the average accuracy and the time-consuming for detection of the target objects in the data sets. These algorithm evaluation indicators evaluate the relative applicability of the two algorithms in practical applications. The reasons are also analyzed for the deficiencies of the two algorithms in the target detection process. It is concluded that the Faster R-CNN algorithm is more suitable for practical applications that require higher target detection accuracy, and the YOLOv3 is more suitable for practical applications that require less time-consuming.
This paper uses the measured atmospheric coherence length profile data of DCIM lidar to analyze the effect of different regularization parameter selection strategies on the inversion of atmospheric turbulence profile. The criterions of L-curve, generalized cross-validation(GCV), quasi-optimal are used respectively, The inversion results is evaluated by signal-tonoise ratio(SNR) and root mean square error(RMSE). The results show that the GCV criterion perform more stable for various measurements than L-curve and quasi-optimal criterion.
We develop differential column image motion (DCIM) lidar for monitoring atmosphere refractive structure constant Cn2 profile. It is important to use an appropriate regularization method for DCIM lidar since the ill-posedness of the integral equation between the Cn2 profile and the measured r0 profile. In this paper, three typical regularization methods are studied to retrieve the Cn2 profiles from r0 profiles.The experiments illustrate that the Tikhonov method and truncated SVD method perform good performance, while damped SVD shows poorer inversion accuracy.
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