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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Bibliographic Details
Published in:Mathematical and computer modelling Vol. 29; no. 3; pp. 101 - 125
Main Authors: Wu, H., Chen, G.
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.02.1999
Elsevier Science
Subjects:
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 Access:Get full text
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Summary: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.
ISSN:0895-7177
1872-9479
DOI:10.1016/S0895-7177(99)00034-5