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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Proceedings ArticleDOI
Robust primary user identification using compressive sampling for cognitive radios
Eva Lagunas,Montse Najar +1 more
TL;DR: This paper introduces a feature-based technique for primary user's spectrum identification with interference immunity which works with a reduced amount of data and detects which channels are occupied by primary users and also identifies the primary users transmission powers without ever reconstructing the signals involved.
DissertationDOI
Sparse Array Signal Processing: New Array Geometries, Parameter Estimation, and Theoretical Analysis
TL;DR: A new array called the super nested array is introduced, which has many of the good properties of the nested array, and at the same time achieves reduced mutual coupling, and is introduced in this thesis.
Journal ArticleDOI
A Sparse Recovery Method for DOA Estimation Based on the Sample Covariance Vectors
TL;DR: The proposed approach not only has higher resolution and ability of processing coherent sources without the need of decorrelation preprocessing, but also exhibits robust performance, especially in the case of low signal-to-noise ratio and/or small number of snapshots.
Journal ArticleDOI
A likelihood-based hyperparameter-free algorithm for robust block-sparse recovery
TL;DR: A novel hyperparameter-free algorithm is proposed for robust recovery of block-sparse signals with single/multiple measurement vector(s), which offers superior recovery performance with incoherent dictionary, as well as greater robustness against highly coherent dictionary.
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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