仅测距信息可用的编队卫星自主相对导航简化无损卡尔曼滤波方法

摘要

For problem of autonomous relative navigation for satellite formation with ranging information only,first,using the analytic method to analyze the observability and the condition that satisfies observability is given.Then,for characteristics of linear state equation and nonlinear measurement equation,design simplified unscented Kalman filter (SUKF) algorithm,there is no need to extend the dimension of state variable,and use the time update of standard Kalman filter in instead of unscented transformationof UKF,which can reduce the computational complexity effectively under the premise of guaranteeing the filtering estimation accuracy,and it is convenient to be used in real time.Finally the simulation results show that the estimation accuracy of SUKF is higher than traditional EKF algorithm,and can achieve the positioning accuracy of meter degree,which is applicable for the occasion where high accuracy relative navigation is not necessary,and can also be used as a backup plan when measuring angle fails.%针对仅测距信息可用条件下编队卫星自主相对导航问题,首先使用解析方法对仅测距条件下系统可观性进行分析,给出满足系统可观的条件,然后针对线性状态方程和非线性量测方程的特点,设计了简化UKF(simplified unscented Kalman filter,SUKF)滤波算法.使用标准卡尔曼滤波中的时间更新代替UKF中的无迹变换,无需对状态变量进行扩维,可以在保证滤波估计精度的前提下有效地减少运算量,便于实时应用.最后的仿真结果表明在仅测距信息可用时,SUKF的滤波估计精度高于传统的EKF算法,相对定轨精度在米级,对于相对导航精度要求不高的场合是适用的;同时也可以作为测角失效时的备份方案.