RLS Wiener FIR Predictor and Filter Based on Innovation Approach in Linear Discrete-Time Stochastic Systems

Abstract

This paper designs the recursive least-squares (RLS) Wiener finite impulse response (FIR) predictor and filter, based on the innovation approach, in linear discrete-time stochastic systems. It is assumed that the signal is observed with additive white noise and the signal process is uncorrelated with the observation noise process. This paper also presents the recursive algorithms for the estimation error variance functions of the proposed RLS Wiener FIR predictor and filter. A numerical simulation example shows the estimation characteristics of the RLS Wiener FIR predictor and filter. Specifically, the estimation characteristics of the proposed RLS Wiener FIR filter and predictor are compared with those of the existing RLS Wiener FIR filter and the RLS Wiener predictor, derived based on the existing RLS Wiener filter, respectively.