Accuracy Analysis of Sigma-Point Kalman Filters
Sigma-point Kalman filters are new filters with high precision aimed at nonlinear system. Within the framework of linear minimum variance recursive algorithm, the accuracy of state estimation using the sigma-point Kalman filters mainly depends on the strategies of choosing sigma-points. In this paper, theorems are presented to determine the relationship between the sigma-point Kalman filters estimate accuracy about the means and variances and the strategies of choosing sigma-points. Then, some deductions about the accuracy of unscented Kalman filter (UKF), divided difference filter (DDF) and Gaussian-Hermite filter (GHF) are presented. The accuracy analysis of state estimation via the sigma-point Kalman filters can benefit from these theorems and deductions.
Nonlinear Filtering Accuracy Analysis Unscented Kalman Filter (UKF) Gaussian-Hermite Filter (GHF) Divided Difference Filter (DDF) Eztended Kalman Filter (EKF)
FAN Wei LI Yong
National Laboratory of Space Intelligent ControlBeijing Institute of Control EngineeringBeijing 10 R&D Center, China Academy of Space Technology, Beijing 100094
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2883-2888
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）