18
Robust RLS Wiener Signal Estimators for Discrete-Time Stochastic Systems with Uncertain Parameters
Authors: Seiichi Nakamori
Number of views: 414
This paper proposes the robust recursive least-squares (RLS) Wiener fixed-point smoother
and filter in linear discrete-time stochastic systems with parameter uncertainties. The uncertain
parameters exist in the observation matrix and the system matrix. The uncertain parameters cause
to generate the degraded signal. In this paper, the degraded signal process is fitted to the finite
order autoregressive (AR) model. The robust RLS Wiener estimators use the system matrices and
the observation matrices for both the signal and the degraded signal, the variance of the state
vector for the degraded signal, the cross-variance of the state vector for the signal with the state
vector for the degraded signal, and the variance of the white observation noise. Also, this paper
proposes the robust recursive fixed-point smoother and filter, by using the covariance information
of the state vector for the degraded signal, the cross-covariance information of the state vector
for the signal with the state vector for the degraded signal, the observation matrices for both the
degraded signal and the signal besides the variance of the white observation noise. In estimating
the signal process expressed by the second order AR model, the proposed robust RLS Wiener filter
is superior in estimation accuracy to the robust Kalman filter and the RLS Wiener filter.