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Gauss, Kalman and advances in recursive parameter estimation
Author(s) -
Young Peter C.
Publication year - 2011
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1187
Subject(s) - kalman filter , computer science , estimation , fast kalman filter , ensemble kalman filter , data assimilation , estimation theory , instrumental variable , gauss , control theory (sociology) , variable (mathematics) , extended kalman filter , mathematics , econometrics , mathematical optimization , algorithm , artificial intelligence , machine learning , economics , geography , control (management) , meteorology , mathematical analysis , management , physics , quantum mechanics
The paper considers how the Kalman filter has influenced the development of recursive parameter estimation since the publication of Rudolf Kalman's seminal article in 1960. It will present a partial review of developments over the past half century and provide a tutorial introduction to the refined instrumental variable approach to the optimal recursive estimation of parameters in both discrete and continuous‐time transfer function models. The paper concludes with a case study that shows how recursive parameter estimation and the Kalman filter can be combined in the design and development of a real‐time adaptive forecasting and data assimilation system for flow in river systems. Copyright © 2010 John Wiley & Sons, Ltd.