H∞ filtering for noise reduction using a total least squares estimation approach

A noise reduction algorithm for signals corrupted by additive unknown L₂ white noise is proposed using an H^∞ filtering framework. The proposed algorithm consists of two steps: a signal enhancement step and a parameter estimation step, which are iterated at each instant. To weaken the dependence between the signal enhancement step and the parameter estimation step, a total least squares estimation step for the dynamical model parameters needed in the H^∞ filtering is introduced. The effectiveness of the proposed algorithm under low signal-to-noise ratio environments is demonstrated by simulation