For detection and estimation of 2-D frequencies from a 2-D array of data using a subspace decomposition method, one needs to construct a block Hankel matrix. For reliable detection andestimation, the rank of the block Hankel matrix should be made equal to the number of 2-D frequenciesinherent in the data in the absence of noise. In this work, we provide the conditions for achieving the desired ran
This dissertation is concerned with the task of efficiently and accurately tracking the singular val...
Spectral estimation is an important classical problem that has received considerable attention in th...
A new method is presented for estimating the column space (signal subspace) of a low rank data matri...
Subspace identification techniques have gained widespread acceptance as a method of obtaining a low-...
Revised version.International audienceIn this paper we analyse the performance of 2-D ESPRIT method ...
International audienceWe develop a parametric high-resolution method for the estimation of the frequ...
We develop a parametric high-resolution method for the estimation of the frequency nodes of linear c...
In this paper, an algorithm for 2-D frequency estimation is proposed. This algorithm consists of two...
In Principal Component Analysis (PCA), the dimension of the signal subspace is detected by counting ...
Two recent approaches (Van Overschee, De Moor, N4SID, Automatica 30 (1) (1994) 75; Verhaegen, Int. J...
The nuclear norm is an effective proxy for matrix rank in a range of minimization problems, includin...
noAbstract: We develop a novel approach to estimate the n unknown constituent frequencies of a sinu...
Subspace-based methods are popular for analysis of two-dimensional data that can be modeled by sums ...
Several well known problems in signal processing are formulated within a general framework of struct...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer En...
This dissertation is concerned with the task of efficiently and accurately tracking the singular val...
Spectral estimation is an important classical problem that has received considerable attention in th...
A new method is presented for estimating the column space (signal subspace) of a low rank data matri...
Subspace identification techniques have gained widespread acceptance as a method of obtaining a low-...
Revised version.International audienceIn this paper we analyse the performance of 2-D ESPRIT method ...
International audienceWe develop a parametric high-resolution method for the estimation of the frequ...
We develop a parametric high-resolution method for the estimation of the frequency nodes of linear c...
In this paper, an algorithm for 2-D frequency estimation is proposed. This algorithm consists of two...
In Principal Component Analysis (PCA), the dimension of the signal subspace is detected by counting ...
Two recent approaches (Van Overschee, De Moor, N4SID, Automatica 30 (1) (1994) 75; Verhaegen, Int. J...
The nuclear norm is an effective proxy for matrix rank in a range of minimization problems, includin...
noAbstract: We develop a novel approach to estimate the n unknown constituent frequencies of a sinu...
Subspace-based methods are popular for analysis of two-dimensional data that can be modeled by sums ...
Several well known problems in signal processing are formulated within a general framework of struct...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer En...
This dissertation is concerned with the task of efficiently and accurately tracking the singular val...
Spectral estimation is an important classical problem that has received considerable attention in th...
A new method is presented for estimating the column space (signal subspace) of a low rank data matri...