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Comparison of linear distributed‐parameter filters to lumped approximants
Author(s) -
Cooper D. J.,
Ramirez W. F.,
Clough D. E.
Publication year - 1986
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690320203
Subject(s) - kalman filter , convergence (economics) , mathematics , noise (video) , filter (signal processing) , control theory (sociology) , computer science , statistics , control (management) , artificial intelligence , economics , image (mathematics) , computer vision , economic growth
Optimal distributed‐parameter filters are commonly implemented using approximating lumped Kalman filtering theory. The effect of such an approximation is investigated. A theoretical development shows that there is a loss in the spatial noise correlation for the lumped approximants. Two numerical examples of engineering significance illustrate that one result of this loss is slower filter convergence for the lumped approximants relative to the full distributed‐parameter filters.