Robust RLS Wiener State Estimators In Linear Discrete-Time Stochastic Systems With Uncertain Parameters
Journal Title: Computer Reviews Journal - Year 2019, Vol 4, Issue 0
Abstract
This paper proposes the robust estimation technique for the signal and the state variables with respect to the state-space model having the general types of system matrices in linear discrete-time stochastic systems with the uncertain parameters. It is assumed that the signal and degraded signal processes are fitted to the finite order autoregressive (AR) models. By fitting the signal process to the AR model, the system matrix for the signal is transformed into the controllable canonical form. By using the system matrix, the existing robust RLS Wiener filter and fixed-point smoother are adopted to estimate the signal. Concerning the state estimation, the existing robust RLS Wiener filter calculates the filtering estimate of the signal. By replacing the observed value with the robust filtering estimate of the signal in the existing RLS Wiener filtering and fixed-point smoothing algorithms, the robust filtering and fixed-point smoothing estimates of the signal and the state variables are calculated. The simulation result shows the superior estimation characteristics of the proposed robust estimation technique for the signal and the state variables in comparison with the existing RLS Wiener filter, the robust Kalman filter, and the existing robust RLS Wiener filter and fixed-point smoother.
Authors and Affiliations
Seiichi Nakamori
Keywords
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