A self-optimization mechanism for generalized adaptive notch smoother

Abstract

Tracking of nonstationary narrowband signals is often accomplished using algorithms called adaptive notch filters (ANFs). Generalized adaptive notch smoothers (GANSs) extend the concepts of adaptive notch filtering in two directions. Firstly, they are designed to estimate coefficients of nonstationary quasi-periodic systems, rather than signals. Secondly, they employ noncausal processing, which greatly improves their accuracy and can be applied whenever additional delay can be tolerated. The paper develops a novel performance assessment mechanism for GANS. It allows one to evaluate tracking accuracy of the smoother without prior knowledge of the true values of the system's frequency or coefficients. The extension can be employed to build a parallel bank of filters, which automatically chooses the one which is best matched to unknown and possibly time-varying tracking conditions.