Minimum variance filters and mixed spectrum estimation

Abstract

This paper presents a spectral density estimator based on a normalized minimum variance (MV) estimator as the one proposed by Lagunas. With an equivalent frequency resolution, this new estimator preserves the amplitude estimation lost in Lagunas one. This proposition comes from a theoretical study of MV filters that highlights this amplitude lost. Two signal types are taken into account: periodic deterministic signals (narrow-band spectral structures) and stationary random signals (broad-band spectral structures). Without selecting a smoothing window, the proposed estimator is an alternative to Fourier-based estimator and, without modeling the signal, it is a concurrent to high-resolution estimators.