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| The Infrared Unfolding State Sensing System of GF-7 Camera Baffle |
| LIU Jing-lei1, 2, FENG Hao1, CAO Xu1, JIANG Chang-hong1, JIA He1, ZHANG Zhang1 |
1. Beijing Institute of Space Mechanics & Electricity, Beijing 100191, China
2. Key Laboratory for Nondestructive Spacecraft Landing Technology of China Academy of Space Technology, Beijing 100191, China |
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Abstract The resolution of space remote sensing satellites is getting finer and finer, and the aperture is getting large and large, which is restricted by the diameter of the carrier. So the baffle system of flexible, deployment has been applied to remote sensors gradually. The monitoring of the geometry state of the space flexible deployable structure in orbit plays an important role in the determination of the main mission. This paper introduces an infrared unfolding state sensing system which is applied to the in-orbit unfolding state monitoring of GF-7 camera baffle system. Firstly, this paper introduces the design of GF-7 camera baffle system and analyzes the special function of infrared sensingduring the unfolding state. A novel unfolded state perception method based on infrared ray transmission barrier characteristics by using lenticular boom as the transmission channel is proposed. The system includes the infrared ray generation system and receiving system, and the 890 nm wavelength light generated by the infrared ray generation system using as the data carrier. The data passing through the flexible deployable structure is converted into an electrical signal by the infrared ray receiving system and the flexible deployment unfolding state information is obtained. The system has the advantages of no moving parts, low power consumption and data bandwidth requirements, and has no mechanical impact on the deployment process. The feasibility of this method is proved through theoretical analysis, and an experiment is designed to verify the optical power of the infrared ray generation system in this method. According to the experiment, the optical power of the infrared system is greater than 25 mA, and the pull-up resistance of the extremely ray receiving system is greater than 15 kΩ, which is the ideal bound for the system. The infrared unfolding state sensing system can effectively determine the state under 125 ℃ when the pull-up resistance is no more than 50 kΩ. For the monitoring design and the application strategy in the environment of in-orbit of the same type of the flexible deployment baffle system, it provides the basis of test data and has a board application prospect.
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Received: 2019-07-24
Accepted: 2019-11-29
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[1] TONG Xu-dong(童旭东). Spacecraft Recovery & Remote Sensing(航天返回与遥感), 2018, 39(4): 18.
[2] LIU Jian-jun, ZHANG Jun, LI Zhao, et al(刘建军，张 俊，李 曌，等). Geomatics World(地理信息世界), 2018, 25(6): 58.
[3] CAO Xu, TANG Ming-zhang, ZHU Mei-fang, et al(曹 旭，唐明章，竺梅芳，等). Analysis of Conceptual Design and Key Technologies of Inflatable Deployable Sunshield(一种充气展开式遮光罩概念设计及关键技术初步分析). The 19th Conference on Remote Sensing of China(第19届中国遥感大学), 2014. 585.
[4] LI Rong, WANG Sen, SHI Hu-li(李 蓉，王 森，施浒立). Infrared and Laser Engineering(红外与激光工程), 2013, (11): 2974.
[5] JIANG Fan, WU Qing-wen, WANG Zhong-su, et al(江 帆，吴清文，王忠素，等). Infrared and Laser Engineering(红外与激光工程), 2016, 45(9): 0918002-1.
[6] CHEN Ke, JI Chao, CAI Zhan-xiu, et al(陈 可，季 超，蔡占秀，等). Chinese Journal of Sensors and Actuators(传感技术学报), 2017, 30(1): 77. |
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