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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Journal ArticleDOI
Sparse covariance-based high resolution time delay estimation for spread spectrum signals
H.R. Park,Jian Li +1 more
TL;DR: A sparse covariance-based estimation algorithm that was originally proposed for direction-of-arrival estimation is adopted and its superiority is verified, especially in correlated signal environments.
Journal ArticleDOI
Time of Arrival and Angle of Arrival Estimation Algorithm in Dense Multipath
TL;DR: In this article, an estimator for the LOS TOA and angle-of-arrival (AOA) in multipath conditions, by obtaining an approximate distribution for the received signals of all the antennas, is presented.
Journal ArticleDOI
Urban surface reconstruction in SAR tomography by graph-cuts
TL;DR: A surface segmentation algorithm based on the computation of the optimal cut in a flow network can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces.
Proceedings ArticleDOI
Joint covariance estimation with mutual linear structure
Ilya Soloveychik,Ami Wiesel +1 more
TL;DR: A new efficient algorithm is developed discovering the structure of these covariance matrices and using it to improve the estimation and derive an upper performance bound of the proposed algorithm in the Gaussian scenario and compared with the Cramér-Rao lower bound.
Proceedings ArticleDOI
A numerical implementation of gridless compressed sensing
TL;DR: The complexity of the proposed algorithm is proportional to the complexity of a single-parameter search in the parameter space and thus in many interesting cases, including frequency estimation it enjoys fast realization.
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.
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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.
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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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