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Deep Neural Network-Based 4-Quadrant Analog Sun Sensor Calibration

Sun, Qinbo and Redondo Gutierrez, Jose Luis and Yu, Xiaozhou (2023) Deep Neural Network-Based 4-Quadrant Analog Sun Sensor Calibration. Space: Science & Technology. American Association for the Advancement of Science (AAAS). doi: 10.34133/space.0024. ISSN 2692-7659.

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Official URL: https://spj.science.org/doi/full/10.34133/space.0024


Many error sources influence the calibration experiment of 4-quadrant sun sensors, making the calibration of sun sensors cumbersome and its accuracy difficult to improve. Any continuous function on a bounded closed set can be approximated by a deep neural network. This paper uses the deep neural network model to approximate the error model. The data-driven training network is adopted to continuously modify the model parameters to fit the error compensation model and ensure that the accuracy reaches the target requirements after calibration. Considering that the deep neural network model needs a considerable amount of data, the neural network model training is divided in 2 stages. In the preliminary stage, cubic surface fitting is used to generate a dataset, which is small in size and controllable. After the completion of the initial training, the experimental data are used to fine-tune the model to achieve error compensation. The accuracy can be improved from 1deg (1-sigma) to 0.1 deg (1-sigma) after the incident angle of the sun sensor is corrected. The error compensation model eliminates the loss of accuracy caused by the distortion of light spots at the edge of the field angle and provides a favorable condition for the expansion of the field angle of the 4-quadrant analog sun sensor.

Item URL in elib:https://elib.dlr.de/194520/
Document Type:Article
Title:Deep Neural Network-Based 4-Quadrant Analog Sun Sensor Calibration
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sun, QinboSchool of Astronautics, Northwestern Polytechnical University, Xi’an, ChinaUNSPECIFIEDUNSPECIFIED
Redondo Gutierrez, Jose LuisUNSPECIFIEDhttps://orcid.org/0000-0002-0037-2299UNSPECIFIED
Date:27 March 2023
Journal or Publication Title:Space: Science & Technology
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:American Association for the Advancement of Science (AAAS)
Keywords:Calibration, Sun Sensor, Neural Network
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transportation
DLR - Research area:Raumfahrt
DLR - Program:R RP - Space Transportation
DLR - Research theme (Project):R - Project ReFEx - Reusability Flight Experiment
Location: Bremen
Institutes and Institutions:Institute of Space Systems > Navigation and Control Systems
Deposited By: Redondo Gutierrez, Jose Luis
Deposited On:12 Apr 2023 12:34
Last Modified:19 Apr 2023 11:38

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