Open AccessOn adaptive filtering for high dimensional systems under parameter uncertainty and its application to satellite data assimilation in oceanography
Author(s) -
S. Hoang,
Rémy Baraille,
Olivier Talagrand,
Xavier Carton,
P. De Mey
Publication year - 2016
Publication title -
journal of computer science and cybernetics (vietnam academy of science and technology)/journal of computer science and cybernetics
Language(s) - English
Resource type - Journals
eISSN - 2815-5939
pISSN - 1813-9663
DOI - 10.15625/1813-9663/13/2/7987
Subject(s) - kalman filter , data assimilation , ensemble kalman filter , alpha beta filter , invariant extended kalman filter , computer science , adaptive filter , fast kalman filter , control theory (sociology) , extended kalman filter , filter (signal processing) , reduction (mathematics) , satellite , algorithm , mathematics , meteorology , engineering , moving horizon estimation , artificial intelligence , geography , aerospace engineering , geometry , control (management) , computer vision
In this paper, the adaptive filtering theory, recently proposed and developed the authors of present work [1-9] for stochastic, encountered in the field of data as simulation in meteorology and oceanography, is reviewed. Several important questions on numerical estimation og the gain matrix, model reduction, structural choices for the gain, filter stability… are discussed. We show the connections of present approach with a standard Kalman filtering. Adaptive filter is implemented along with a Kalman filtering. Adaptive filter is implemented along with a Kalman filter and standard Newton relation method on the four-layer adiabatic Miami Isopycnical Co-ordinate Ocean Model (MICOM) to produce the estimate for the deep oceanic circulation using assimilate synthetic observations of surface height. Numerical results justify high efficiency of the adaptive filter whose performance is slightly better than that of a Kalman filter due to impossibility to correctly specify the error statistics in a Kalman filter.