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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Proceedings ArticleDOI
Extended Target Reconstruction of Airborne Real Aperture Array Radar by Adaptive Hybrid Regularization
TL;DR: In this paper , an adaptive hybrid regularization (AHR) method is proposed by a data-adaptive reweighted strategy, where the generalized sparse (GS) regularization norm and the generalized total variation (GTV) norm are combined to enhance the angular resolution and scale information of extended targets simultaneously.
Journal ArticleDOI
Array auto-calibration using a generalized least-squares method
TL;DR: A new class of auto-calibration algorithms, based on a generalized least-squares (GLS) cost function, which supports both gain-phase calibration and mutual coupling calibration and it is independent of array shape is introduced.
Proceedings ArticleDOI
A Beamspace Multi-sources DOA Estimation Method for UAV Cluster Systems
TL;DR: In this paper , a beamspace DOA matrix method was proposed to reduce the RF chain number by beamspace signal processing, and a rotation invariance between beam clusters was established to deal with the random phase difference.
Proceedings ArticleDOI
Underdetermined direction of arrival estimation with coprime array constrained by approximated zero norm
Wu Yan,Wang Cheng,Liu Jianyu +2 more
TL;DR: Aiming at the problem of underdetermined DOA estimation of coprime array, a sparse reconstruction algorithm with approximated zero norm constraint is proposed and results show that this method has higher estimation accuracy and resolution than traditionalDOA estimation algorithms.
Direction-of-Arrival Estimation for Constant Modulus Signals Using a Structured Matrix Recovery Technique
TL;DR: In this article , a structured matrix recovery technique (SMART) was proposed for CM DOA estimation via fully exploiting the Vandermonde structure of the steering matrix and the CM structure of source signals.
References
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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.
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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