Open AccessNon‐minimal state‐space polynomial form of the Kalman filter for a general noise model
Author(s) -
Wilson E.D.,
Clairon Q.,
Taylor C.J.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.3577
Subject(s) - kalman filter , state space representation , representation (politics) , control theory (sociology) , noise (video) , state space , mathematics , state variable , filter (signal processing) , state (computer science) , variable (mathematics) , adaptive filter , polynomial , computer science , algorithm , control (management) , artificial intelligence , statistics , mathematical analysis , image (mathematics) , physics , politics , political science , law , computer vision , thermodynamics
The optimal refined instrumental variable method for the estimation of the Box–Jenkins (BJ) model is modified so that it functions as an optimal filter and state‐estimation algorithm. In contrast to the previously developed minimal and non‐minimal state‐space (NMSS) forms for an Auto‐Regressive Moving Average with eXogenous variables (ARMAX) model, the new algorithm requires the introduction of a novel extended NMSS form. This facilitates representation of the more general noise component of the BJ model. The approach can be used for adaptive filtering and state variable feedback control.