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

页数：7

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