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
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Gridless Multidimensional Angle of Arrival Estimation for Arbitrary 3D Antenna Arrays
TL;DR: In this paper, a compressed sensing-based method is proposed to extract multi-dimensional AoA information exploiting the sparse nature of the signal received by a sensor array, which enables accurate gridless resolution of the AoA in systems with arbitrary 3D antenna arrays.
Proceedings ArticleDOI
Persistently active block sparsity with application to direction-of-arrival estimation of moving sources
Jimeng Zheng,Mostafa Kaveh +1 more
TL;DR: A new method is developed by introducing a novel objective function, which exploits both block-level and element-level sparsities and promotes persistence in activity within a block and a SVD-based method is used to reduce its computational complexity.
Proceedings ArticleDOI
A novel off-grid DOA estimation via weighted subspace fitting
TL;DR: A novel off-grid direction-of-arrival (DOA) estimation algorithm involving sparse recovery is proposed based on weighted subspace fitting, in which multiple snapshots are used and effects of off- grid DOA are taken into account.
Fast Sparse Non-Negative Least Squares via ADMM for High Resolution DOA Estimation
TL;DR: In this article , a sparse non-negative least squares (NNLS) problem is solved by the alternating direction method of multipliers (ADMM) at a low-computational complexity.
Proceedings ArticleDOI
Compressive subspace fitting for multiple measurement vectors
TL;DR: This work shows that the noise robustness of these algorithms can be significantly improved by allowing sequential subspace estimation and support filtering, even when the number of snapshots is insufficient.
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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