Paper: An adaptive robustizing approach to kalman filtering

Authors:
Chee Tsai;Ludwik Kurz
Affiliations:
Department of Electrical Engineering and Computer Science, Polytechnic Institute of New York, Brooklyn, NY 11201, U.S.A.;Department of Electrical Engineering and Computer Science, Polytechnic Institute of New York, Brooklyn, NY 11201, U.S.A.
Venue:
Automatica (Journal of IFAC)
Year:
1983

Citing 2
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Suboptimal Kalman filtering for linear systems with Gaussian-sum type of noise

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Abstract

The performance of a linear Kalman filter will degrade when the dynamic noise is not Gaussian. A robust Kalman filter based on the m-interval polynomial approximation (MIPA) method for unknown non-Gaussian noise is proposed. Two situations are considered: (a) the state is Gaussian and the observation noise is non-Gaussian; (b) the state is non-Gaussian and the observation noise is Gaussian. It is shown, as compared with other non-Gaussian filters, the MIPA Kalman filter is computationally feasible, unbiased, more efficient and robust. For the scalar model, Monte Carlo simulations are given to demonstrate the ideas involved.