Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
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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
On gridless sparse methods for multi-snapshot DOA estimation
TL;DR: This paper unify the two gridless sparse methods for direction of arrival (DOA) estimation by interpreting GLS as atomic norm methods in various scenarios and provides theoretical guarantees of GLS for DOA estimation in the case of limited snapshots.
Journal ArticleDOI
An Off-Grid DOA Estimation Method Using Proximal Splitting and Successive Nonconvex Sparsity Approximation
TL;DR: The extensive simulations are conducted to verify that the proposed method achieves superior resolution and more accurate DOA estimation performance than other conventional sparsity-based method and the state-of-the-art off-grid methods in many cases of practical interest.
Journal ArticleDOI
Frequency Estimation From Arbitrary Time Samples
Kaushik Mahata,Mashud Hyder +1 more
TL;DR: This work considers the problem of estimating the line spectrum of a signal from finitely many time domain samples, and presents a gridless algorithm for solving the total variation minimization approach associated with this problem.
Journal ArticleDOI
SPICE-Based SAR Tomography over Forest Areas Using a Small Number of P-Band Airborne F-SAR Images Characterized by Non-Uniformly Distributed Baselines
Xing Peng,Xinwu Li,Changcheng Wang,Jianjun Zhu,Lei Liang,Haiqiang Fu,Yanan Du,Zefa Yang,Qinghua Xie +8 more
TL;DR: A sparse iterative covariance-based estimation approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas that can successfully reconstruct the vertical structure of a forest and shows a better performance than W&o-CS, beamforming, Capon, and the iterative adaptive approach.
Journal ArticleDOI
Correlation Subspaces: Generalizations and Connection to Difference Coarrays
Chun-Lin Liu,P.P. Vaidyanathan +1 more
TL;DR: It is demonstrated through examples that using sparse arrays and generalized correlation subspaces, DOA estimators with source priors exhibit better estimation performance than those without priors, in extreme cases like low SNR and limited snapshots.
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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