Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
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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
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
TL;DR: An overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost and is elaborated by a wide range of applications in signal processing, communications, and machine learning.
Proceedings ArticleDOI
Sparsity-based DOA estimation using co-prime arrays
TL;DR: To fully utilize the virtual aperture achieved in the difference co-array constructed from a co-prime array structure, sparsity-based spatial spectrum estimation technique is exploited and results in increased degrees of freedom as well as improved DOA estimation performance.
Journal ArticleDOI
Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies
TL;DR: The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework and customized implementations exploiting the measurements collected at a unique time instant and multiple time instants are presented and discussed.
Journal ArticleDOI
On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
TL;DR: This paper presents a gridless version of SPICE (gridless SPICE, or GLS), which is applicable to both complete and incomplete data without the knowledge of noise level, and proves the equivalence between GLS and atomic norm-based techniques under different assumptions of noise.
References
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Journal ArticleDOI
A sparse signal reconstruction perspective for source localization with sensor arrays
TL;DR: This work presents a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold that has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources.
Journal ArticleDOI
Applications of second-order cone programming
TL;DR: In this paper, an efficient primal-dual interior-point method for solving second-order cone programs (SOCP) is presented. But it is not a generalization of interior point methods for convex problems.
Journal ArticleDOI
Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares
TL;DR: This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing and shows that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX).
Applications of Second Order Cone Programming
TL;DR: A significant special case of the problems which could be solved were those whose constraints were given by semidefinite cones, and these have a wide range of applications, some of which are discussed in Section 5, and can still be solved efficiently using interior point methods.
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