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
A Study on Adaptive Sparse Matrix Beamforming Algorithm of Error Beam Steering Vector for Target Estimation
Journal ArticleDOI
Three More Decades in Array Signal Processing Research: An optimization and structure exploitation perspective
TL;DR: The signal processing community is currently witnessing the emergence of sensor array processing and direction-of-arrival estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, and drone surveillance as mentioned in this paper .
Journal ArticleDOI
Underlying topography and forest height estimation from SAR tomography based on a nonparametric spectrum estimation method with low sidelobes
TL;DR: In this paper , a nonparametric spectrum estimation method with low sidelobes is introduced to solve the problem of the inversion of forest structural parameters, which can improve the estimation accuracy for the underlying topography and forest height.
Posted Content
New challenges in covariance estimation: multiple structures and coarse quantization
TL;DR: In this paper, the authors revisited a fundamental problem of multivariate statistics: estimating covariance matrices from finitely many independent samples, based on massive MIMO systems.
Journal ArticleDOI
Joint RFI mitigation and radar echo recovery for one-bit UWB radar
TL;DR: In this paper , the authors proposed a one-bit weighted SPICE (SParse Iterative Covariance-based Estimation) based framework for the joint RFI mitigation and radar echo recovery of a one bit UWB radar.
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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