Journal ArticleDOI
Robust adaptive beamforming based on interference covariance matrix sparse reconstruction
TLDR
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.About:
This article is published in Signal Processing.The article was published on 2014-03-01. It has received 165 citations till now. The article focuses on the topics: Covariance matrix & Adaptive beamformer.read more
Citations
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Journal ArticleDOI
A Robust and Efficient Algorithm for Coprime Array Adaptive Beamforming
TL;DR: This paper decomposes the coprime array into a pair of sparse uniform linear subarrays and process their received signals separately, and proposes a novel coprimes array adaptive beamforming algorithm, where both robustness and efficiency are well balanced.
Journal ArticleDOI
Source Estimation Using Coprime Array: A Sparse Reconstruction Perspective
TL;DR: This paper proposes a novel sparse reconstruction-based source estimation algorithm by using a coprime array that combines a difference coarray derived from a copRime array as the foundation for increasing the number of DOFs and a virtual uniform linear subarray covariance matrix sparse reconstruction to solve the power estimation problem.
Journal ArticleDOI
Compressive sensing-based coprime array direction-of-arrival estimation
TL;DR: The authors first generate a random compressive sensing kernel to compress the received signals of coprime array to lower-dimensional measurements, which can be viewed as a sketch of the original received signals to perform high-resolution direction-of-arrival (DOA) estimation.
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
Interference-plus-Noise Covariance Matrix Reconstruction via Spatial Power Spectrum Sampling for Robust Adaptive Beamforming
TL;DR: A novel method named spatial power spectrum sampling (SPSS) is proposed to reconstruct the INC matrix more efficiently, with the corresponding beamforming algorithm developed, where the covariance matrix taper (CMT) technique is employed to further improve its performance.
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.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
Journal ArticleDOI
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
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