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
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Proceedings ArticleDOI
Multi-resolution BCS-based approach for DoA estimation
TL;DR: The problem of the estimation of the number and angles of arrival of electromagnetic signals impinging on a linear array of sensors is addressed by means of a multi-resolution approach based on the Bayesian Compressive Sensing (BCS).
DissertationDOI
New Directions In Sparse Sampling and Estimation For Underdetermined Systems
TL;DR: A new paradigm of underdetermined estimation that explicitly establishes the fundamental interplay between sampling, statistical priors and the underlying sparsity, leads to exciting future research directions in a variety of application areas, and gives rise to new questions that can lead to stand-alone theoretical results in their own right.
Proceedings ArticleDOI
Off-Grid Underdetermined DOA Estimation of Quasi-stationary Signals via Sparse Bayesian Learning
TL;DR: An expectation-maximization iteration method is developed to estimate DOAs of QSS based on the off-grid model from a Bayesian perspective that does not need estimate parameters in performing the algorithms and has better estimation precision.
Proceedings ArticleDOI
DOA estimation based on sparse covariance vector representation using two-channel receiver
Mohammad Jabbarian-Jahromi,Ghasem Foudazi,Karim Mohammadpour-Aghdam,Masoudreza Mohammad-Salehi +3 more
TL;DR: A practical representation of the sparse signal model is proposed for direction finding applications which extends conventional phase-only interferometry to incorporate the covariance matrix of received signal which is reshaped in a vector.
Proceedings ArticleDOI
Hyperparameter-free DOA estimation under power constraints
TL;DR: A direction of arrival (DOA) estimation algorithm that embeds a weighting scheme in the objective function without selection of any hyperparameters and is robust to the assumption of uncorrelated sources is proposed.
References
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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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