ISBN: 978-1-84821-277-0This chapter contains sections titled: Model, concept of subspace, definition of high resolution ; MUSIC ; Determination criteria of the number of complex sine waves ; The MinNorm method ; "Linear" subspace methods ; The ESPRIT method ; Illustration of the subspace-based methods performance ; Adaptive research of subspaces ; Integrating a priori known frequencies into the MUSIC criterion ; Bibliograph
Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper...
Three sinusoidal decomposition methods are described. They are the total least squares principal eig...
Recent frequency domain identification algorithms based on subspace based techniques are discussed. ...
ISBN: 978-1-84821-277-0This chapter contains sections titled: Model, concept of subspace, definition...
Subspace based estimation using decomposition techniques such as the SVD is a powerful tool in many ...
A multitude of applications contain signals that can be well described as being formed as a sum of s...
Abstract—Subspace methods such as MUSIC, Minimum Norm, and ESPRIT have gained considerable attention...
A novel data covariance model has recently been proposed for the subspace-based estimation of multip...
Subspace-based parameter estimators, like HTLS in nuclear magnetic resonance spectroscopy, are effic...
In this paper a novel method for multiple one dimensional real valued sinusoidal signal frequency es...
Includes bibliographical references.Subspace methods such as MUSIC, Minimum Norm, and ESPRIT have ga...
In this paper, we present a subspace-based fundamental fre-quency estimator based on an extension of...
Abstract—The problem of modeling a signal segment as a sum of exponentially damped sinusoidal compon...
The interactions between the signal processing and matrix computation areas is explored by examinin...
Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper...
Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper...
Three sinusoidal decomposition methods are described. They are the total least squares principal eig...
Recent frequency domain identification algorithms based on subspace based techniques are discussed. ...
ISBN: 978-1-84821-277-0This chapter contains sections titled: Model, concept of subspace, definition...
Subspace based estimation using decomposition techniques such as the SVD is a powerful tool in many ...
A multitude of applications contain signals that can be well described as being formed as a sum of s...
Abstract—Subspace methods such as MUSIC, Minimum Norm, and ESPRIT have gained considerable attention...
A novel data covariance model has recently been proposed for the subspace-based estimation of multip...
Subspace-based parameter estimators, like HTLS in nuclear magnetic resonance spectroscopy, are effic...
In this paper a novel method for multiple one dimensional real valued sinusoidal signal frequency es...
Includes bibliographical references.Subspace methods such as MUSIC, Minimum Norm, and ESPRIT have ga...
In this paper, we present a subspace-based fundamental fre-quency estimator based on an extension of...
Abstract—The problem of modeling a signal segment as a sum of exponentially damped sinusoidal compon...
The interactions between the signal processing and matrix computation areas is explored by examinin...
Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper...
Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper...
Three sinusoidal decomposition methods are described. They are the total least squares principal eig...
Recent frequency domain identification algorithms based on subspace based techniques are discussed. ...