Non-minimal state-space polynomial form of the Kalman filter for a general noise model

作者：Wilson E D^*; Clairon Q; Taylor C J

来源：Electronics Letters, 2018, 54(4): 204-206.

DOI：10.1049/el.2017.3577

• state-space methods
• polynomials
• Kalman filters
• state estimation
• autoregressive moving average processes
• nonminimal state-space polynomial form
• Kalman filter
• general noise model
• optimal refined instrumental variable method
• Box-Jenkins model estimation
• BJ model estimation
• optimal filter
• state-estimation algorithm
• NMSS
• autoregressive moving average with exogenous variables model
• ARMAX model
• general noise component representation
• adaptive filtering
• state variable feedback control

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.

• 出版日期2018-2-22