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
OMP-based DOA estimation performance analysis
TL;DR: A new performance guarantee for Orthogonal Matching Pursuit (OMP) in the context of the Direction Of Arrival (DOA) estimation problem is presented.
Journal ArticleDOI
Robust Sparse Bayesian Learning for Off-Grid DOA Estimation With Non-Uniform Noise
TL;DR: A robust sparse Bayesian learning method that can maintain excellent DOA estimation performance with uniform or non-uniform noise and achieve satisfactory performance under a coarse grid condition is proposed.
Journal ArticleDOI
A sparse sampling strategy for angular superresolution of real beam scanning radar
Yin Zhang,Junjie Wu,Jianyu Yang +2 more
TL;DR: The sparse sampling model of RBSR is described as an underdetermined equation-solving problem, the received signals are sparsely recovered in target domain, and simulation results show that compressive sampling methods can recover the target domain accurately, especially under the condition of high signal-to-noise ratio (SNR).
Proceedings ArticleDOI
Structured Channel Covariance Estimation from Limited Samples in Massive MIMO
TL;DR: In this paper, a Maximum-Likelihood (ML) massive MIMO covariance estimator based on a parametric representation of the channel angular spread function (ASF) is proposed.
Journal ArticleDOI
Fast Frequency Estimation With Prior Information
Kaushik Mahata,Md. Mashud Hyder +1 more
TL;DR: A fast gridless method for sparse frequency estimation in the presence of prior information that allows arbitrarily sampled data and has a significantly smaller complexity than the existing gridless sparse recovery methods is presented.
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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