The problem of estimating the spectral content of exponentially decaying signals from a set of irregularly sampled data is of considerable interest in several applications. for example in various forms of radio frequency spectroscopy. in this paper. we propose a new nonparametric iterative adaptive approach that provides a Solution to this estimation problem As opposed to commonly used methods in the field, the damping coefficient, or linewidth, is explicitly modeled. which allows for all Improved estimation performance Numerical examples Using both simulated data and data from NQR experiments Illustrate he benefits of the proposed estimator as compared to Currently available lion parametric methods (C) 2009 Elsevier Inc All rights reserve
Several algorithms for the spectral analysis of irregularly sampled random processes can estimate th...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
One important class of problems within spectral estimation is when the signal can be well represente...
Spectral estimation can be defined as the art of recovering the frequency content in a measured sign...
Estimation of high-resolution multidimensional spectra from unevenly sampled limited sized data sets...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most ...
International audienceA novel framework is proposed for the estimation of multiple sinusoids from ir...
Several algorithms for the spectral analysis of irregularly sampled random processes can estimate th...
Several algorithms for the spectral analysis of irregularly sampled random processes can estimate th...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
One important class of problems within spectral estimation is when the signal can be well represente...
Spectral estimation can be defined as the art of recovering the frequency content in a measured sign...
Estimation of high-resolution multidimensional spectra from unevenly sampled limited sized data sets...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
This article introduces a data-adaptive nonparametric approach for the estimation of time-varying sp...
Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most ...
International audienceA novel framework is proposed for the estimation of multiple sinusoids from ir...
Several algorithms for the spectral analysis of irregularly sampled random processes can estimate th...
Several algorithms for the spectral analysis of irregularly sampled random processes can estimate th...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
One important class of problems within spectral estimation is when the signal can be well represente...