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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Proceedings ArticleDOI
Decoupled Direction-of-Arrival Estimations Using Relative Harmonic Coefficients
TL;DR: In this article, a decoupled source direction-of-arrival (DOA) estimation algorithm using relative harmonic coefficients (RHC) is proposed. But the proposed algorithm requires the source's elevation and azimuth to be estimated separately.
Journal ArticleDOI
Iterative methods for DOA estimation of correlated sources in spatially colored noise fields
TL;DR: Two iterative approaches to jointly estimate the angle parameters and unknown nonuniform noise covariance matrix from multi-snapshot sensor array data are developed and allow to obtain the estimates by explicit formulas.
Proceedings ArticleDOI
Gridless compressed sensing for fully augmentable arrays
TL;DR: This paper presents a DOA estimation algorithm for FAAs without assuming uncorrelated sources, based on the newly introduced gridless SPARse ROW-norm reconstruction (SPARROW) formulation for the joint sparse reconstruction from multiple measurement vectors that outperforms the existing algorithms in the presence of correlated signals or small number of snapshots.
Proceedings ArticleDOI
Sparse representation-based method for two-dimensional direction-of-arrival estimation with L-shaped array
TL;DR: A novel sparse representation-based method for two-dimensional (2-D) direction-of-arrival (DOA) estimation with L-shaped array with higher estimation accuracy than the existing subspace-based and sparse representations-based methods.
Proceedings ArticleDOI
DOA estimation in monostatic MIMO array based on sparse signal reconstruction
TL;DR: In this paper, a novel method for direction arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) array is presented by using the sparse signal reconstruction of monostastic MIMO array measurements with an overcomplete basis, the singular value decomposition of the received data matrix can be penalties based on the l 1 -norm.
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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