Modified Unscented Kalman Filter for Relative Navition of Space Target Acquisition, Tracking and Control

At present, the trend of improving the mobility of fast acquisition, long-term tracking and high-precision control has been formed in China. The continuous observation ability of non-cooperative targets and good imaging performance in motion are important trends in the development of optical remote...

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Bibliographic Details
Published in2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) pp. 1 - 5
Main Authors Kang, Jianbing, Zhang, Ai, Shi, Zhaoxin, Miao, Yuanming, Zhao, Xin, Zhang, Chao
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2022
Subjects
acquision
control
Estimation
Filtering algorithms
Optical filters
relative navigation
Satellite navigation systems
Satellites
space targets
Space vehicles
Target tracking
tracking
unscented Kalman filter
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Summary:At present, the trend of improving the mobility of fast acquisition, long-term tracking and high-precision control has been formed in China. The continuous observation ability of non-cooperative targets and good imaging performance in motion are important trends in the development of optical remote sensing technology. Aiming at the high-precision observation and real-time tracking requirements of high orbit space satellites, this paper studies the high-precision relative navigation of space non cooperative targets. In order to solve the problem that the estimation accuracy of Unscented Kalman filter (UKF) decreases when the system visibility is low, a modified Unscented Kalman filter (MUKF) based on the visibility is proposed. This algorithm defines a system visibility characterization method based on the error gain matrix of the filtering process, and proposes the visibility scaling parameter based on this method, The filter gain covariance matrix is adjusted online, so that the algorithm can adjust the weight of state prediction and system observation online according to the observability of the current time. Numerical simulation shows that compared with UKF, the estimation accuracy of MUKF is improved by about 4 times, and MUKF stabilizes faster and has higher accuracy.
DOI:10.1109/ICARCE55724.2022.10046586