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

Authors:
H. Wu;G. Chen
Affiliations:
Department of Electrical and Computer Engineering University of Houston, Houston, TX 77204-4793, U.S.A.;Department of Electrical and Computer Engineering University of Houston, Houston, TX 77204-4793, U.S.A.
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1999

Citing 4
Cited 0

Kalman filtering with real-time applications

Kalman filtering with real-time applications
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Abstract

This paper develops several suboptimal filtering algorithms for discrete-time linear systems that have state and/or measurement noise of the Gaussian-sum type. These new computational schemes are modifications and generalizations of the well-known algorithms of Sorenson and Alspach and of Masreliez. Under the common minimum mean square estimation criterion, these new schemes are derived as recursive computational algorithms. Monte Carlo simulations have shown that these new filtering algorithms significantly improve the computational efficiency and/or filtering performance of the existing algorithms.