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
More filters
Journal ArticleDOI
Two Novel DOA Estimation Approaches for Real-Time Assistant Calibration Systems in Future Vehicle Industrial
TL;DR: An off-grid DOA estimation algorithm based on sparse Bayesian learning (SBL) is proposed in this paper, and the temporal correlation between the neighboring snapshot numbers is considered in the off- grid algorithm.
Journal ArticleDOI
Fast communication: A sparse representation scheme for angle estimation in monostatic MIMO radar
TL;DR: The proposed sparse representation scheme for direction of arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar works well for coherence targets without any decorrelation procedure, and has low sensitivity to the priori information of the target number.
Journal ArticleDOI
Wideband Spectrum Sensing From Compressed Measurements Using Spectral Prior Information
Daniel Romero,Geert Leus +1 more
TL;DR: This paper uses the asymptotic theory of circulant matrices to propose a dimensionality reduction technique that simplifies existing structured covariance estimation algorithms, achieving a similar performance at a much lower computational cost.
Journal ArticleDOI
A Sparse-Based Approach for DOA Estimation and Array Calibration in Uniform Linear Array
TL;DR: A joint estimation of direction-of-arrival (DOA) and array perturbations is proposed under a unified optimization framework by utilizing the sparsity of both the DOAs and perturbation matrix.
Journal ArticleDOI
Multi-pitch estimation exploiting block sparsity
TL;DR: An efficient algorithm for solving the resulting optimization problem is devised exploiting a novel variable step-size alternating direction method of multipliers (ADMM), which has guaranteed convergence and shows notable robustness to the f0 vs f 0 / 2 ambiguity problem.
References
More filters
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.
Related Papers (5)
Two decades of array signal processing research: the parametric approach
Hamid Krim,Mats Viberg +1 more