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, is a concurrent to high resolution estimators.
Complete list of metadatas

Cited literature [12 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00374817
Contributor : Nadine Martin <>
Submitted on : Monday, December 6, 2010 - 3:39:15 PM
Last modification on : Friday, September 6, 2019 - 3:00:06 PM
Long-term archiving on : Saturday, December 3, 2016 - 1:42:00 AM

File

Durnerin-Martin_SP_2000.pdf
Publisher files allowed on an open archive

Identifiers

Collections

CNRS | UGA | LIS

Citation

Matthieu Durnerin, Nadine Martin. Minimum Variance Filters and Mixed Spectrum Estimation. Signal Processing, Elsevier, 2000, 80 (12), pp.2597-2608. ⟨10.1016/S0165-1684(00)00137-7⟩. ⟨hal-00374817v2⟩

Share

Metrics

Record views

250

Files downloads

551