Volume 40 Issue 6
Nov.  2020
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ZHANG He, QIN Weiwei, ZHOU Cheng, SONG Hengxin, HUA Yufeng, WANG Yu. Attitude Determination Algorithm for Micro-satellite Based on High-order UKF Using Information Fusion[J]. Journal of Space Science, 2020, 40(6): 1091-1101. doi: 10.11728/cjss2020.06.1091
Citation: ZHANG He, QIN Weiwei, ZHOU Cheng, SONG Hengxin, HUA Yufeng, WANG Yu. Attitude Determination Algorithm for Micro-satellite Based on High-order UKF Using Information Fusion[J]. Journal of Space Science, 2020, 40(6): 1091-1101. doi: 10.11728/cjss2020.06.1091

Attitude Determination Algorithm for Micro-satellite Based on High-order UKF Using Information Fusion

doi: 10.11728/cjss2020.06.1091
  • Received Date: 2019-08-12
  • Rev Recd Date: 2020-02-07
  • Publish Date: 2020-11-15
  • To improve the attitude determination accuracy of miniature low-cost attitude sensors, a micro-satellite attitude determination algorithm that combines magnetometer and solar sensor observations was designed, based on high-order Unscented Kalman Filter (UKF) and the attitude sensor configuration of magnetometer/solar sensor/gyroscope. Firstly, in order to improve the one-step prediction accuracy of the nonlinear system state equation, the high-order UKF algorithm with fifth-order UT transformation was used to increase the number of Sigma sampling points and improve the system state prediction accuracy. Secondly, owing to the shortcoming of the single observation vector filtering algorithm that couldn't coordinate multiple observation data with different dimension simultaneously, an information fusion filtering algorithm using two observation vectors was proposed, which was based on the observations of the magnetometer and the solar sensor. The Kalman gain was obtained by the information corresponding to the geomagnetic vector and the solar vector through the gain calculation of the UKF algorithm. Consequently, the Gaussian probability density criterion was used to fuse the Kalman gain information, and the fused information was used to correct the value of the one-step prediction. Therefore, this algorithm reduced the observation errors of the attitude quaternions. Finally, the simulation results proved the availability of the proposed method.

     

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