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Modal Kalman Filter
Author(s) -
Mohammaddadi Gh.,
Pariz N.,
Karimpour A.
Publication year - 2017
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1425
Subject(s) - extended kalman filter , control theory (sociology) , invariant extended kalman filter , modal , kalman filter , taylor series , series (stratigraphy) , alpha beta filter , fast kalman filter , filter (signal processing) , ensemble kalman filter , nonlinear filter , nonlinear system , computer science , filter design , mathematics , moving horizon estimation , physics , mathematical analysis , artificial intelligence , materials science , computer vision , control (management) , polymer chemistry , paleontology , quantum mechanics , biology
In the Extended Kalman Filter (EKF), only the first‐order term of the Taylor series is employed. Hence, the nonlinearities in the system dynamics are not fully considered. In the proposed method, to overcome this drawback, the higher‐order terms of the Taylor series are considered and a new filter, based on the Modal series, is designed. In this paper, based on the Modal series and careful approximations, a nonlinear filter is converted to a series of linear filters, and the extracted filter is named the Modal Kalman Filter (MKF). The efficiency and advantage of MKF are analytically proven and its applicability examined with some simulations.