Optical–Mechanical Integration Analysis and Validation of LiDAR Integrated Systems with a Small Field of View and High Repetition Frequency
-
Published:2024-02-16
Issue:2
Volume:11
Page:179
-
ISSN:2304-6732
-
Container-title:Photonics
-
language:en
-
Short-container-title:Photonics
Author:
Li Lu12ORCID, Xing Kunming2, Zhao Ming23, Wang Bangxin2, Chen Jianfeng24ORCID, Zhuang Peng5
Affiliation:
1. School of Mechanical and Automotive Engineer, West Anhui University, Lu’an 237012, China 2. Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China 3. School of Electronic Engineering, Huainan Normal University, Huainan 232038, China 4. Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China 5. Anhui Lanke Information Technology Co., Ltd., Hefei 230088, China
Abstract
Integrated systems are facing complex and changing environments with the wide application of atmospheric LiDAR in civil, aerospace, and military fields. Traditional analysis methods employ optical software to evaluate the optical performance of integrated systems, and cannot comprehensively consider the influence of optical and mechanical coupling on the optical performance of the integrated system, resulting in the unsatisfactory accuracy of the analysis results. Optical–mechanical integration technology provides a promising solution to this problem. A small-field-of-view LiDAR system with high repetition frequency, low energy, and single-photon detection technology was taken as an example in this study, and the Zernike polynomial fitting algorithm was programmed to enable transmission between optical and mechanical data. Optical–mechanical integration technology was employed to obtain the optical parameters of the integrated system under a gravity load in the process of designing the optical–mechanical structure of the integrated system. The experimental validation results revealed that the optical–mechanical integration analysis of the divergence angle of the transmission unit resulted in an error of 2.586%. The focal length of the telescope increased by 89 μm, its field of view was 244 μrad, and the error of the detector target surface spot was 4.196%. The continuous day/night detection results showed that the system could accurately detect the temporal and spatial variations in clouds and aerosols. The inverted optical depths were experimentally compared with those obtained using a solar photometer. The average optical depth was 0.314, as detected using LiDAR, and 0.329, as detected by the sun photometer, with an average detection error of 4.559%. Therefore, optical–mechanical integration analysis can effectively improve the stability of the structure of highly integrated and complex optical systems.
Funder
Key Project of Natural Science Research of Anhui Provincial Department of Education high-level talent research start-up project of West Anhui University the Strategic Priority Research Program of the Chinese Academy of Sciences Civil Aerospace Technology Advance Research Project Key Program of the 13th 5-year plan, CASHIPS 2019 Anhui Province Science and Technology Major Project
Reference26 articles.
1. Research status and progress of Lidar for atmosphere in China (Invited);Di;Infrared Laser Eng.,2021 2. Optical-mechanical system design, installation and performance test of lidar with small-field and high-repetition frequency;Li;Infrared Laser Eng.,2021 3. Lolli, S., Vivone, G., Lewis, J.R., Sicard, M., Welton, E.J., Campbell, J.R., Comerón, A., D’Adderio, L.P., Tokay, A., and Giunta, A. (2019). Overview of the new version 3 NASA Micro-Pulse Lidar Network (MPLNET) automatic precipitation detection algorithm. Remote Sens., 12. 4. Lolli, S., D’ Adderio, L.P., Campbell, J.R., Sicard, M., Welton, E.J., Binci, A., Rea, A., Tokay, A., Comerón, A., and Barragan, R. (2018). Vertically resolvedprecipitation intensity retrieved through a synergy between the ground-based NASA MPLNET lidar network measurements, surface disdrometer datasets and an analytical model solution. Remote Sens., 10. 5. Overview of MPLNET version 3 cloud detection;Lewis;J. Atmos. Ocean. Technol.,2016
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|