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
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
Covariance Matching Estimation Techniques for Array Signal Processing Applications
TL;DR: A general framework for covariance matching techniques is presented and it is shown that they are well suited to solve several problems arising in array signal processing.
Journal ArticleDOI
Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing
Arian Maleki,David L. Donoho +1 more
TL;DR: It is shown that the phase transition is a well-defined quantity with the suite of random underdetermined linear systems chosen, and the optimally tuned algorithms dominate such proposals.
Journal ArticleDOI
New Method of Sparse Parameter Estimation in Separable Models and Its Use for Spectral Analysis of Irregularly Sampled Data
Petre Stoica,Prabhu Babu,Jian Li +2 more
TL;DR: A new semiparametric/sparse method is introduced, called SPICE, which is computationally quite efficient, enjoys global convergence properties, can be readily used in the case of replicated measurements and, unlike most other sparse estimation methods, does not require any subtle choices of user parameters.
Journal ArticleDOI
Monotonic convergence of a general algorithm for computing optimal designs
TL;DR: Monotonic convergence is established for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney for computing optimal designs.
Journal ArticleDOI
Monotonic convergence of a general algorithm for computing optimal designs
TL;DR: In this paper, the authors established monotonic convergence for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist. 14 (1978) 1379−1389] for computing optimal designs.
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