Density Approximation Error Assessment and Compensation in Point-Mass Filter

被引：0
|
作者：
Matousek, J. [1 ]
Dunik, J. [1 ]
Straka, O. [1 ]
Blasch, E. [2 ]
机构：
[1] Univ West Bohemia, Dept Cyber, Univ 8, Plzen 30614, Czech Republic
[2] Air Force Res Lab, Rome, NY 13441 USA
来源：
2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022) | 2022年
关键词：
State estimation; Point-mass filter; Density approximation; Approximation error; DESIGN;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper deals with the state estimation of non-linear stochastic dynamic systems with an emphasis on a probability density function approximation used by point-mass filters. Approximation error of the standard point-mass density is analysed and quantified, and a novel point-mass density approximation with inherent approximation error minimisation is developed. The properties of the proposed point-mass are theoretically analysed and numerically illustrated.
引用
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页数：7
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