Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
TLDR
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
An efficient sparse representation algorithm for DOA estimation in MIMO radar system
TL;DR: An efficient sparse representation algorithm for direction of arrival (DOA) estimation in a multiple-input multiple-output (MIMO) Radar system is proposed and provides better angle estimation performance for both uncorrelated targets and correlated targets.
Proceedings ArticleDOI
Covariance Estimation in Elliptical Models with Convex Structure
Ilya Soloveychik,Ami Wiesel +1 more
TL;DR: In this paper, the covariance matrices of non-Gaussian distributions with convex structure are estimated using the General Method of Moments (GMM) approach, and the Tyler's estimator can be obtained as a solution of a convexly relaxed GMM problem.
Journal ArticleDOI
An efficient off-grid direction-of-arrival estimation method based on inverse-free sparse Bayesian learning
Yixin Yang,Long Yang +1 more
Posted Content
ChainNet: Neural Network-Based Successive Spectral Analysis
TL;DR: In this article, a neural network-based direction of arrival estimation scheme is proposed, which tackles the estimation task as a multidimensional classification problem and uses a classification chain with as many stages as the number of sources.
Dissertation
Sparse covariance fitting for source location
TL;DR: A new algorithm for finding the angles of arrival of multiple uncorrelated sources impinging on a uniform linear array of sensors is proposed, based on sparse signal representation and does not require either the knowledge of the number of the sources or a previous initialization.
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.
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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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