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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Journal ArticleDOI
SPICE and LIKES: Two hyperparameter-free methods for sparse-parameter estimation
Petre Stoica,Prabhu Babu +1 more
TL;DR: The derivation of SPICE is revisited to streamline it and to provide further insights into LIKES, a new method obtained in a hyperparameter-free manner from the maximum-likelihood principle applied to the same estimation problem as considered by SPICE.
Journal ArticleDOI
A Discretization-Free Sparse and Parametric Approach for Linear Array Signal Processing
Zai Yang,Lihua Xie,Cishen Zhang +2 more
TL;DR: An exact discretization-free method, named as sparse and parametric approach (SPA), is proposed for uniform and sparse linear arrays that carries out parameter estimation in the continuous range based on well-established covariance fitting criteria and convex optimization and is statistically consistent under uncorrelated sources.
Journal ArticleDOI
Assistant Vehicle Localization Based on Three Collaborative Base Stations via SBL-Based Robust DOA Estimation
TL;DR: An assistant vehicle localization method based on direction-of-arrival (DOA) estimation based on a sparse Bayesian learning (SBL)-based robust DOA estimation approach is proposed, which shows the effectiveness and superiority of the proposed method.
Journal ArticleDOI
Pushing the Limits of Sparse Support Recovery Using Correlation Information
Piya Pal,P.P. Vaidyanathan +1 more
TL;DR: It is shown that if existing algorithms can recover sparse support of size s, then using such correlation information, the guaranteed size of recoverable support can be increased to O(s2), although the sparse signal itself may not be recoverable.
Journal ArticleDOI
Improved Source Number Detection and Direction Estimation With Nested Arrays and ULAs Using Jackknifing
Keyong Han,Arye Nehorai +1 more
TL;DR: This work proposes a novel strategy, inspired by the jackknifing resampling method, which greatly improves the results of the existing source number detection and DOA estimation methods, based on uniform linear arrays and the newly proposed nested arrays.
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.
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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.
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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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