Marginalized particle filter for spacecraft attitude estimation from vector measurements
Yaqiu LIU
Xueyuan JIANG
Guangfu MA
摘要：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.
机标关键词：attitude estimationthe Kalman filternumerical simulationvector observationsparticle filtersnonlinear modelstate spacein order
分类号：TP3(计算技术、计算机技术)
资助基金：高等学校博士学科点专项科研项目(20050213010)国家高技术研究发展计划(863计划)(2004AA735080)
论文发表日期：2007-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：7( 60-66 )
英文信息