Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
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This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing, obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many- snapshot cases but can be used even in single-snapshot situations.Abstract:
This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing. The proposed approach is obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many-snapshot cases but can be used even in single-snapshot situations. SPICE has several unique features not shared by other sparse estimation methods: it has a simple and sound statistical foundation, it takes account of the noise in the data in a natural manner, it does not require the user to make any difficult selection of hyperparameters, and yet it has global convergence properties.read more
Citations
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Dissertation
Applications of compressive sensing to direction of arrival estimation
TL;DR: In this article, Compressive Sensing (CS) is used to solve the Directions of Arrival (DOA) problem in Array-Signal-Verarbeitung.
Journal ArticleDOI
Sequential DOA estimation method for multi-group coherent signals
TL;DR: This paper presents a novel approach to estimate the direction of arrivals (DOAs) of each group sequentially for multi-group coherent signals, which can estimate the same DOA as long as they are from different groups.
Proceedings ArticleDOI
Parameter estimation of coherently distributed sources using sparse representation
TL;DR: A new estimator of coherently distributed source employing the sparse representation technology is proposed by utilizing subspace fitting principle and the proposed method uses the eigenvalue-decomposition method on the sample covariance matrix of the sensor array received data and obtains the signal eigenvectors.
Journal ArticleDOI
Off-Grid Direction-of-Arrival Estimation Using a Sparse Array Covariance Matrix
TL;DR: An alternating iterative algorithm is presented that exploits the alternating update of a convex optimization problem and a least-squares problem to solve for the two sparse vectors in the resulting array covariance matrix and the off-grid DOA estimation can thus be achieved.
Journal ArticleDOI
High SNR consistent compressive sensing without signal and noise statistics
TL;DR: In this paper, the authors proposed two techniques, namely residual ratio minimization (RRM) and residual ratio thresholding with adaptation (RRTA), to operate OMP algorithm without a priroi knowledge of noise variance and signal sparsity.
References
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Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI
Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones
TL;DR: This paper describes how to work with SeDuMi, an add-on for MATLAB, which lets you solve optimization problems with linear, quadratic and semidefiniteness constraints by exploiting sparsity.
Book
Interior-Point Polynomial Algorithms in Convex Programming
TL;DR: This book describes the first unified theory of polynomial-time interior-point methods, and describes several of the new algorithms described, e.g., the projective method, which have been implemented, tested on "real world" problems, and found to be extremely efficient in practice.
Book
Spectral analysis of signals
Petre Stoica,Randolph L. Moses +1 more
TL;DR: 1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra.
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