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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Journal ArticleDOI
Augmented lagrange based on modified covariance matching criterion method for DOA estimation in compressed sensing.
Weijian Si,Xinggen Qu,Lutao Liu +2 more
TL;DR: A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is presented, in which DOA estimation is considered as the joint sparse recovery from multiple measurement vectors (MMV) using the modified-based covariance matching criterion.
Posted Content
Generalized Residual Ratio Thresholding
TL;DR: A novel technique called generalized residual ratio thresholding (GRRT) is presented for operating SOMP and BOMP without the \textit{a priori} knowledge of signal sparsity and noise variance and derive finite sample and finite signal to noise ratio (SNR) guarantees for exact support recovery.
Journal ArticleDOI
Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range.
Jacob Compaleo,Inder J. Gupta +1 more
TL;DR: In this article, the authors proposed a Spectral Domain Sparse Representation (SDSR) approach for the direction-of-arrival estimation of signals incident to an antenna array.
Journal ArticleDOI
Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation
TL;DR: The reformulate the gridless direction of arrival (DoA) estimation problem in a novel reweighted covariance fitting (CF) method and applies the unified surrogate for Schatten-p quasi-norm with two-factor matrix norms for more tractable and scalable optimization problem.
Proceedings ArticleDOI
Joint 2-D DOA estimation using gridless sparse method
TL;DR: A novel gridless sparse method (GSM) is proposed to estimate two-dimensional (2-D) direction-of-arrival (DOA) using L-shaped arrays using Covariance fitting criterion and semidefinite programming to estimate DOAs in the continuous range.
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.
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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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