Volume 37, Number 6, December 2019
|Page(s)||1158 - 1164|
|Published online||11 February 2020|
Study on Fault Detection of Star-Sensor Based on UKF
Institute of Telecommunication Satellite, CAST, Beijing 100094, China
The fault detection of star sensor in satellite when no telemetry data from the gyroscope can be obtained is investigated. An algorithm based on the unscented Kalman filter (UKF) is proposed to indicate the occurrence of the fault. By adding the angular speed of the satellite into the state equation, the UKF is designed to estimate the angular and the angular speed of the satellite. After that, a comparison between the measured and the estimated value of both the angular and the angular speed is made. In this way, the star sensor fault can be finally detected. Two fault cases (abrupt constant fault and noise increase fault) are taken into consideration in the simulation to show the validness of the present algorithm. Furthermore, a test verification by using the real telemetry data on orbit is performed to demonstrate that the present algorithm can detect the star sensor fault effectively. In application, the developed fault detection algorithm can be employed in the ground measurement and control station to monitor the star sensor fault such that the fault can be detected immediately and the alarm indicating the occurrence of the fault will be given.
针对通信卫星在轨运行后无陀螺遥测数据的星敏故障问题，提出一种基于无迹卡尔曼滤波(unscented kalman filter, UKF)的故障检测算法，将卫星角速度纳入状态方程进行估计，采用2个星敏的输出数据作为测量方程，通过分别比对正常时段和故障时段下卫星角度的稳定值与其估计值、角速度的稳定值与其估计值，可以准确检测出星敏数据故障。针对在轨可能出现的2种故障工况（突变故障、噪声变大故障）进行了仿真，并进一步采用在轨遥测数据进行了试验验证，仿真和试验验证结果均表明，该算法能有效检测出星敏数据故障，可用于地面对在轨星敏数据进行故障监测，以便发现故障后及时进行报警处理。
Key words: unscented kalman filter(UKF) / star-sensor / fault detection
关键字 : UKF / 星敏 / 故障检测
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.