Design of RLS-FIR filter using covariance information in linear continuous-time stochastic systems

This paper addresses a new design method of recursive least-squares (RLS) and finite impulse response (FIR) filter, using covariance information, in linear continuous-time stochastic systems. The signal process is observed with additive white noise. It is assumed that the white observation noise is independent of the signal process. The auto-covariance function of the signal is expressed in the semi-degenerate kernel form. The RLS-FIR filter uses the following information: 1. The auto-covariance function of the signal expressed in the semi-degenerate kernel form. 2. The variance of the white observation noise process. 3. The observed values.