Non‐minimal state‐space polynomial form of the Kalman filter for a general noise model | Zendy

Wilson E.D. | Zendy; Clairon Q. | Zendy; Taylor C.J. | Zendy

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Non‐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 , invariant extended kalman filter , noise (video) , control theory (sociology) , extended kalman filter , alpha beta filter , state space representation , polynomial , mathematics , fast kalman filter , computer science , space (punctuation) , algorithm , moving horizon estimation , mathematical analysis , artificial intelligence , statistics , control (management) , image (mathematics) , operating system

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.

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