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Journal ArticleDOI

SPICE: A Sparse Covariance-Based Estimation Method for Array Processing

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.

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Citations
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Proceedings ArticleDOI

An adaptively focusing measurement design for compressed sensing based DOA estimation

TL;DR: An adaptive design strategy for the measurement matrix for applying Compressed Sensing to Direction Of Arrival (DOA) estimation with antenna arrays is proposed, which achieves a superior DOA estimation performance.
Journal ArticleDOI

Grid-less T.V minimization for DOA estimation

TL;DR: The resulting semidefinite programming approach is a globally convergent, fully parametric method capable of working with two dimensional arrays with any arbitrary sensor configurations, and shows improved performance when compared with other popular alternatives.
Posted Content

Non-Coherent Direction-of-Arrival Estimation Using Partly Calibrated Arrays

TL;DR: It is proved that, under mild conditions, with the non-coherent system of subarrays, it is possible to identify more sources than identifiable by each individual subarray, which has not been investigated before.
Proceedings ArticleDOI

Sparsity-based direction-of-arrival estimation for strictly non-circular sources

TL;DR: A novel strategy to take the NC signal structure into account for the SSR, which results in a two-dimensional SSR problem and addresses the 2-D off-grid problem by proposing a low-complexity procedure that estimates the sources' grid offset from the closest neighboring grid points.
Journal ArticleDOI

On Gridless Sparse Methods for Multi-snapshot Direction of Arrival Estimation

TL;DR: Two techniques for gridless sparse methods for direction of arrival estimation in the presence of multiple snapshots are unify by interpreting theoretically GLS as atomic norm methods in various scenarios and under different assumptions of noise.
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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System identification

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Spectral analysis of signals

TL;DR: 1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra.
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