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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)101-125
Journal / PublicationMathematical and Computer Modelling
Volume29
Issue number3
Publication statusPublished - Feb 1999
Externally publishedYes

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.

Research Area(s)

  • Filtering algorithm, Gaussian sum noise, Kalman filter, Suboptimal filtering