Robust Kalman Filtering and Its Applications.
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WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
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This paper presents a robust Kalman filtering algorithm that is obtained assuming a scale contaminated normal distribution for the noise of the measurement equation. The mixture of normals obtained as a posterior distribution is approximated at each stage by a normal distribution with the same mean and variance. The resulting algorithm is simple, has a straightforward interpretation and seems to provide useful robust estimators in several statistical problems that are briefly reviewed. Originator-supplied keywords include Robustness, and mixtures of normals.
- Statistics and Probability