The Kalman filter is widely used in many different fields. Many practical applications and theoretical results show that the Kalman filter is very sensitive to outliers in a measurement process. In this paper some reasons why the Kalman Filter is sensitive to outliers are analyzed and a series of outlier-tolerant algorithms are designed to be used as substitutes of the Kalman Filter. These outlier-tolerant filters are highly capable of preventing adverse effects from outliers similar with the Kalman Filter in complexity degree and very outlier-tolerant in the case there are some outliers arisen in sampling data set of linear stochastic systems. Simulation results show that these modified algorithms are safe and applicable.
%0 Journal Article
%1 IJACSA.2011.021206
%A HU Shaolin Huajiang Ouyang, Karl Meinke S U N Guoji
%D 2011
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K Kalman Linear Outlier-tolerant; Outlier; filter; stochastic system.
%N 12
%T Outlier-Tolerant Kalman Filter of State Vectors in Linear Stochastic System
%U http://ijacsa.thesai.org/
%V 2
%X The Kalman filter is widely used in many different fields. Many practical applications and theoretical results show that the Kalman filter is very sensitive to outliers in a measurement process. In this paper some reasons why the Kalman Filter is sensitive to outliers are analyzed and a series of outlier-tolerant algorithms are designed to be used as substitutes of the Kalman Filter. These outlier-tolerant filters are highly capable of preventing adverse effects from outliers similar with the Kalman Filter in complexity degree and very outlier-tolerant in the case there are some outliers arisen in sampling data set of linear stochastic systems. Simulation results show that these modified algorithms are safe and applicable.
@article{IJACSA.2011.021206,
abstract = {The Kalman filter is widely used in many different fields. Many practical applications and theoretical results show that the Kalman filter is very sensitive to outliers in a measurement process. In this paper some reasons why the Kalman Filter is sensitive to outliers are analyzed and a series of outlier-tolerant algorithms are designed to be used as substitutes of the Kalman Filter. These outlier-tolerant filters are highly capable of preventing adverse effects from outliers similar with the Kalman Filter in complexity degree and very outlier-tolerant in the case there are some outliers arisen in sampling data set of linear stochastic systems. Simulation results show that these modified algorithms are safe and applicable.
},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{HU Shaolin Huajiang Ouyang}, Karl Meinke S U N Guoji},
biburl = {https://www.bibsonomy.org/bibtex/2831d10f1469cb9d563e652dafcc20449/thesaiorg},
interhash = {cd31a0162a842366f4456c3c50503605},
intrahash = {831d10f1469cb9d563e652dafcc20449},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {Kalman Linear Outlier-tolerant; Outlier; filter; stochastic system.},
number = 12,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Outlier-Tolerant Kalman Filter of State Vectors in Linear Stochastic System}},
url = {http://ijacsa.thesai.org/},
volume = 2,
year = 2011
}