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
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Dissertation
Subset selection in signal processing using sparsity-inducing norms
TL;DR: This dissertation deals with different subset selection problems in wireless communications systems and addresses the joint distributed beamforming optimization and relay subset assignment in a multi-user scenario with non-orthogonal transmission and in a scenario with a single source-destination pair.
Journal ArticleDOI
Robust air velocity estimation based on iterative implementation of Minimum Mean-Square Error (MMSE)
TL;DR: A robust air velocity estimator, abbreviated as II-MMSE, is proposed based on the iterative implementation of Minimum Mean-Square Error (MMSE) criterion, which takes account of the noise covariance information in the array observation data in a natural manner and yields good robustness to limited snapshots and array modeling errors.
Proceedings ArticleDOI
Online High Resolution Stochastic Radiation Radar Imaging Using Sparse Covariance Fitting
TL;DR: This paper examines the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency of the SRR imaging methods.
Journal ArticleDOI
Source localization utilizing weighted power iterative compensation via acoustic vector hydrophone array
TL;DR: A sparse signal power estimation method based on sparse covariance matrix fitting criterion (SCMFC) is proposed, which uses the weighted power compensation strategy to address the source localization issue in the condition of low signal-to-noise ratio (SNR) or closely-spaced targets.
Proceedings ArticleDOI
Compressive Sensing Based Direction-of-Arrival Estimation in MIMO Radars in Presence of Strong Jamming via Blocking Matrix
TL;DR: A compressive sensing (CS)-based anti-jamming DOA estimation with only a few snapshots is proposed and it is demonstrated that the proposed method outperforms the traditional methods.
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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