Measures of Non-Gaussianity in Unscented Kalman Filter Framework

被引：0
|
作者：
Straka, Onedrej [1 ]
Dunik, Jindrich [1 ]
Simandl, Miroslav [1 ]
机构：
[1] Univ W Bohemia, European Ctr Excellence, New Technol Informat Soc, Dept Cybernet,Fac Appl Sci, Plzen 30614, Czech Republic
来源：
2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2014年
关键词：
state estimation; nonlinear filters; Kalman filtering; non-Gaussianity measures; nonlinearity measures;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper deals with state estimation of nonlinear stochastic dynamic systems. In particular, the stress is laid on the recursive computation of the higher order moments of the state estimate in a local filter. The higher order moments are then used as a cornerstone of non-Gaussianity measures having the ability to indicate a possible decrease of the estimate quality. A technique is proposed for a general algorithm of the local filters, detailed for the unscented Kalman filter, and illustrated in numerical examples.
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页数：8
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