Add to ORKGTwo important questions in array signal processing are addressed in this paper: the data matrix versus autocorrelation matrix alternative and the recursive implementation of subspace DOA methods. The discussion of the first question is done in face of the proposed class of recursive algorithms. These new algorithms are easily implementable and have a high degree of parallelism that is suitable for on-line implementations. Algorithms for recursive implementation of the eigendecomposition (ED) of the autocorrelation matrix and SVD of the data matrix are described. The ED/SVD trade-off is discussed.Peer Reviewe
This Thesis develops algorithms for the processing of data from an array of sensors. Of particular i...
Many decentralized subspace tracking algorithms has been proposed in the literature, see [1, 2, 5]. ...
Numerical linear algebra, digital signal processing, and parallel algorithms are three disciplines w...
Two important questions in array signal processing are addressed in this paper: the data matrix vers...
Subspace-based algorithms for array signal processing typically begin with an eigenvalue decompositi...
Matrix Singular Value Decomposition (SVD) and its application to problems in signal processing is ex...
Abstract The singular value decomposition (SVD) is a very important tool for narrowband adaptive sen...
Many subspace-based array signal processing algorithms assume that the noise is spatially white. In ...
Abstract—Subspace-based methods rely on singular value de-composition (SVD) of the sample covariance...
The estimation of low rank signals in noise is a ubiquitous task in signal processing, communication...
The Modified Eigenvalue problem arises in many applications such as Array Processing, Automatic Targ...
Includes bibliographical references (page 73)This paper presents the adaptive array statement and di...
The ordinary Singular Value Decomposition (SVD) is widely used in statistical and signal processing...
In this paper, a unified statistical performance analysis using perturbation expansions is applied t...
Subspace-based algorithms are a class of algorithms for estimation problems in array signal processi...
This Thesis develops algorithms for the processing of data from an array of sensors. Of particular i...
Many decentralized subspace tracking algorithms has been proposed in the literature, see [1, 2, 5]. ...
Numerical linear algebra, digital signal processing, and parallel algorithms are three disciplines w...
Two important questions in array signal processing are addressed in this paper: the data matrix vers...
Subspace-based algorithms for array signal processing typically begin with an eigenvalue decompositi...
Matrix Singular Value Decomposition (SVD) and its application to problems in signal processing is ex...
Abstract The singular value decomposition (SVD) is a very important tool for narrowband adaptive sen...
Many subspace-based array signal processing algorithms assume that the noise is spatially white. In ...
Abstract—Subspace-based methods rely on singular value de-composition (SVD) of the sample covariance...
The estimation of low rank signals in noise is a ubiquitous task in signal processing, communication...
The Modified Eigenvalue problem arises in many applications such as Array Processing, Automatic Targ...
Includes bibliographical references (page 73)This paper presents the adaptive array statement and di...
The ordinary Singular Value Decomposition (SVD) is widely used in statistical and signal processing...
In this paper, a unified statistical performance analysis using perturbation expansions is applied t...
Subspace-based algorithms are a class of algorithms for estimation problems in array signal processi...
This Thesis develops algorithms for the processing of data from an array of sensors. Of particular i...
Many decentralized subspace tracking algorithms has been proposed in the literature, see [1, 2, 5]. ...
Numerical linear algebra, digital signal processing, and parallel algorithms are three disciplines w...