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| DOI： |
| Received:November 07, 2006Revised:August 27, 2006 |
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| Marginalized particle filter for spacecraft attitude estimation from vector measurements |
| Yaqiu LIU, Xueyuan JIANG, GuangfuMA |
| (School of Astronautics, Harbin Institute of
Technology, Harbin Heilongjiang 150001, China;Information and Computer Engineering College, Northeast Forestry University, Harbin Heilongjiang 150040, China) |
| Abstract: |
| An algorithm based on the marginalized particle filters (MPF) is given in details in this paper
to solve the spacecraft attitude estimation problem: attitude and gyro bias estimation using the
biased gyro and vector observations. In this algorithm, by marginalizing out the state appearing
linearly in the spacecraft model, the Kalman filter is associated with each particle in order to
reduce the size of the state space and computational burden. The distribution of attitude vector
is approximated by a set of particles and estimated using particle filter, while the estimation of
gyro bias is obtained for each one of the attitude particles by applying the Kalman filter.
The efficiency of this modified MPF estimator is verified through numerical simulation of a fully
actuated rigid body. For comparison, unscented Kalman filter (UKF) is also used to gauge the
performance of MPF. The results presented in this paper clearly demonstrate that the MPF is superior
to UKF in coping with the nonlinear model. |
| Key words: Attitude estimation Particle filter Spacecraft Nonlinear filter Quaternion |
