Journal ArticleDOI
Fully Automatic Computation of Diagonal Loading Levels for Robust Adaptive Beamforming
Lin Du,Jian Li,Petre Stoica +2 more
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TLDR
In this article, an algorithm that can be used to compute the diagonal loading (DL) level completely automatically from the given data without the need of specifying any user parameter is considered.Abstract:
The main drawback of the conventional diagonal loading (DL) approaches is that there is no clear guideline on how to choose the DL level reliably or how to select user parameters appropriately. An algorithm that can be used to compute the DL level completely automatically from the given data without the need of specifying any user parameter is considered. In this algorithm an enhanced covariance matrix estimate obtained via a shrinkage method, instead of the sample covariance matrix, is used in the standard Capon beamforming formulation. The performance of the resulting beamformer is illustrated via numerical examples, and it is compared with several other adaptive beamformers.read more
Citations
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Journal ArticleDOI
Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks
Olivier Ledoit,Michael Wolf +1 more
TL;DR: In this article, a new nonlinear shrinkage estimator of the covariance matrix is proposed, which is more flexible than previous linear shrinkage estimates and has "just the right number of free parameters".
Journal ArticleDOI
Robust Adaptive Beamforming With a Novel Interference-Plus-Noise Covariance Matrix Reconstruction Method
TL;DR: A novel method to reconstruct the interference-plus-noise covariance matrix is proposed that is robust against unknown arbitrary-type mismatches and the nominal steering vector can be corrected via maximizing the beamformer output power by solving a quadratically constrained quadratic programming (QCQP) problem.
Journal ArticleDOI
Robust adaptive beamforming based on interference covariance matrix sparse reconstruction
TL;DR: This paper reconstructs the interference-plus-noise covariance matrix in a sparse way, instead of searching for an optimal diagonal loading factor for the sample covariance Matrix, to demonstrate that the performance of the proposed adaptive beamformer is almost always equal to the optimal value.
Journal ArticleDOI
Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks
Olivier Ledoit,Michael Wolf +1 more
TL;DR: In this paper, a nonlinear shrinkage estimator of the covariance matrix is proposed for portfolio selection, where the number of assets is of the same magnitude as the sample size.
Journal ArticleDOI
Source Enumeration Via MDL Criterion Based on Linear Shrinkage Estimation of Noise Subspace Covariance Matrix
Lei Huang,Hing Cheung So +1 more
TL;DR: The strong consistency of the LS-MDL criterion for m,n→∞ and m/n→ c ∈ (0,∞) is proved, where m and n are the antenna number and snapshot number, respectively, and an accurate estimator for the covariance matrix of the noise subspace components is derived.
References
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Journal ArticleDOI
A well-conditioned estimator for large-dimensional covariance matrices
Olivier Ledoit,Michael Wolf +1 more
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Book
Optimum Array Processing: Part IV of Detection, Estimationand Modulation Theory
TL;DR: Optimum array processing: part IV of detection, estimation and modulation theory, Optimum arrayprocessing: part III of detection- estimation-modulation theory.