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 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
Kaushik Mahata,Mashud Hyder +1 more
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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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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