摘要
卫星轨道确定问题中可能存在初始估计信息误差较大、状态及测量误差分布不是高斯分布等问题,为了寻求一个能采用解决这两种问题的方法,本文采用了“采样-重要性-重采样”SIR(Sampling Importance Resampling)粒子滤波算法作为滤波方法,以地磁场矢量为测量量,对低地球轨道卫星的轨道数据进行自主的估计.为了避免该滤波算法中的采样贫乏的问题,采用了一个崎岖化方法来克服采样贫乏问题.最后给出了此方法应用到了卫星轨道确定问题中的数字仿真实例.
The great information error of initial estimation and non-Gaussian distribution of state and measurement error may exist in the satellite orbit determination problem. To solve these two problems, the sampling-importance-resampling（SIR） particale filter is used to estimate the satellite orbit. The filter measurement information is obtained from the magnetometer＇ s measurement. To avoid the sampling sparse problem of this filter method, a rough method has been used. Finally a simulation is given to estimate the satellite orbit.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2005年第4期573-577,共5页
Control Theory & Applications
基金
863-701青年基金资助项目(2002AA717016).
