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
DOA Estimation for Sparse Array via Sparse Signal Reconstruction
Nan Hu,Zhongfu Ye,Xu Xu,Ming Bao +3 more
TL;DR: Two methods of direction-of-arrival (DOA) estimation for sparse array are proposed, based on different optimization problems, which are solvable using second-order cone (SOC) programming.
Journal ArticleDOI
Weighted SPICE: A unifying approach for hyperparameter-free sparse estimation☆
TL;DR: This paper establishes a connection between SPICE and the l 1 -penalized LAD estimator as well as the square-root LASSO method and evaluates the four methods mentioned above in a generic sparse regression problem and in an array processing application.
Journal ArticleDOI
A Sparse Representation Method for DOA Estimation With Unknown Mutual Coupling
Jisheng Dai,Dean Zhao,Xiaofu Ji +2 more
TL;DR: This letter describes a modified sparse representation method in the presence of unknown mutual coupling that takes advantage of the special structure of the mutual coupling matrix (MCM) for uniform linear arrays (ULAs) so as to eliminate the Mutual coupling effect completely.
Journal ArticleDOI
Super-Resolution Surface Mapping for Scanning Radar: Inverse Filtering Based on the Fast Iterative Adaptive Approach
TL;DR: Simulation results and real data processing demonstrate that the proposed FIAA-based inverse filtering outperforms the existing super-resolution approaches in resolution improvement and results in a higher computational efficiency.
Journal ArticleDOI
Sparse Spatial Spectral Estimation: A Covariance Fitting Algorithm, Performance and Regularization
Jimeng Zheng,Mostafa Kaveh +1 more
TL;DR: It is proved the asymptotic, in the number of snapshots, consistency of SpSF estimators of the DOAs and the received powers of uncorrelated sources in a sparse spatial spectra model.
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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