1 Minimum Variance Filters and Mixed Spectrum Estimation
نویسنده
چکیده
Résumé Cet article présente un estimateur de densité spectrale défini à partir d’un estimateur du Minimum de Variance (MV) Normalisé tel que celui proposé par Lagunas. Avec une résolution fréquentielle équivalente, l’objectif de ce nouvel estimateur est de préserver l’estimation de l’amplitude contrairement à l’estimateur de Lagunas. Cette proposition s’appuie sur l’étude de la fonction de transfert du filtre MV. Deux types de signaux sont considérés: des signaux déterministes périodiques (dont la structure spectrale est à bande étroite) et des signaux aléatoires stationnaires (dont la structure spectrale est à large bande). Sans avoir à choisir une fenêtre d’apodisation, l’estimateur proposé est une alternative aux estimateurs de Fourier, et, sans appliquer de modèle au signal, est un concurrent des estimateurs paramétriques
منابع مشابه
New Adaptive Filter For The Removal Of Mixed Noise In Images With Fine Detail Preservation
In this paper, we developed an algorithm to remove additive mixed noise in images with the preservation of fine details and edges. The noise characteristics may vary in the same application from one image to another. In these environments, nonlinear general filters will not perform well and adaptive non-linear filters are best suited. The algorithm based on local statistics such as signal varia...
متن کاملDiscrete-Time State Estimation Using Unbiased FIR Filters with Minimized Variance
Optimal or unbiased estimators are widely used for state estimation and tracking. We propose a new minimum variance unbiased (MVU) finite impulse response (FIR) filter which minimizes the estimation error variance in the unbiased FIR (UFIR) filter. The relationship between the filter gains of the MVU FIR, UFIR and optimal FIR (OFIR) filters is found analytically. Simulations provided using a po...
متن کاملExtension of minimum variance estimation for systems with unknown inputs
In this paper, we address the problem of minimum variance estimation for discrete-time time-varying stochastic systems with unknown inputs. The objective is to construct an optimal filter in the general case where the unknown inputs affect both the stochastic model and the outputs. It extends the results of Darouach and Zasadzinski (1997) where the unknown inputs are only present in the model. ...
متن کاملSuperresolution Sar Imaging Algorithm Based on Mvm and Weighted Norm Extrapolation
In this paper, we present an extrapolation approach, which uses minimum weighted norm constraint and minimum variance spectrum estimation, for improving synthetic aperture radar (SAR) resolution. Minimum variance method is a robust high resolution method to estimate spectrum. Based on the theory of SAR imaging, the signal model of SAR imagery is analyzed to be feasible for using data extrapolat...
متن کاملWeak Lensing Reconstruction and Power Spectrum Estimation: Minimum Variance Methods
Large-scale structure distorts the images of background galaxies, which allows one to measure directly the projected distribution of dark matter in the universe and determine its power spectrum. Here we address the question of how to extract this information from the observations. We derive minimum variance estimators for projected density reconstruction and its power spectrum and apply them to...
متن کامل