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.

HU Shaolin, Huajiang Ouyang, Karl Meinke and SUN Guoji. “Outlier-Tolerant Kalman Filter of State Vectors in Linear Stochastic System”. International Journal of Advanced Computer Science and Applications (IJACSA) 2.12 (2011). http://dx.doi.org/10.14569/IJACSA.2011.021206

@article{Shaolin2011,
title = {Outlier-Tolerant Kalman Filter of State Vectors in Linear Stochastic System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.021206},
url = {http://dx.doi.org/10.14569/IJACSA.2011.021206},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {12},
author = {HU Shaolin and Huajiang Ouyang and Karl Meinke and SUN Guoji}
}