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

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....

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Veröffentlicht in:	Mathematical and computer modelling Jg. 29; H. 3; S. 101 - 125
Hauptverfasser:	Wu, H., Chen, G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.02.1999 Elsevier Science
Schlagworte:	Exact sciences and technology Filtering algorithm Gaussian sum noise Inference from stochastic processes; time series analysis Kalman filter Mathematics Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use Statistics Stochastic processes Suboptimal filtering Filtering algorithm Suboptimal filtering Gaussian sum noise Kalman filter Monte Carlo method Error analysis Covariance matrix Algorithm Unbiased estimation Linear system Gaussian sum Gaussian noise Optimal filtering Kalman filtering Combined treatment Linear estimation Sorenson Alspach scheme State noise
ISSN:	0895-7177, 1872-9479
Online-Zugang:	Volltext
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