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

作　　者：Qinbo Sun Jose Luis Redondo Gutiérrez Xiaozhou Yu 

机构地区：[1]School of Astronautics,Northwestern Polytechnical University,Xi’an,China [2]Institute of Space Systems,German Aerospace Center,Cologne,Germany [3]Dalian University of Technology,Dalian,China

出　　处：《Space(Science & Technology)》2023年第1期193-205,共13页空间科学与技术（英文）

基　　金：the support provided by the National Key Research and Development Program of China(project no.2021YFE0116000);the Open Research of State Key Laboratory of Structural Analysis for Industrial Equipment(project no.GZ20103).

摘　　要：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 1°(1σ)to 0.1°(1σ)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.

关 键 词：NEURAL DEEP network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]