Journal ArticleDOI
Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies
TLDR
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.Abstract:
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. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.read more
Citations
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Journal ArticleDOI
Compressive Sensing in Electromagnetics - A Review
TL;DR: A review of the state-of-the-art and most recent advances of compressive sensing and related methods as applied to electromagnetics can be found in this article, where a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed.
Book ChapterDOI
Sparse methods for direction-of-arrival estimation
TL;DR: An overview of these sparse methods for DOA estimation is provided, with a particular highlight on the recently developed gridless sparse methods, e.g., those based on covariance fitting and the atomic norm.
Journal ArticleDOI
A Discretization-Free Sparse and Parametric Approach for Linear Array Signal Processing
Zai Yang,Lihua Xie,Cishen Zhang +2 more
TL;DR: An exact discretization-free method, named as sparse and parametric approach (SPA), is proposed for uniform and sparse linear arrays that carries out parameter estimation in the continuous range based on well-established covariance fitting criteria and convex optimization and is statistically consistent under uncorrelated sources.
Journal ArticleDOI
Off-Grid DOA Estimation Using Sparse Bayesian Learning in MIMO Radar With Unknown Mutual Coupling
TL;DR: In this paper, a sparse Bayesian learning (SBL)-based method for estimating the direction of arrival (DOA) in a multiple-input and multiple-output (MIMO) radar system with unknown mutual coupling effect between antennas is investigated.
Journal ArticleDOI
Underdetermined DOA Estimation Under the Compressive Sensing Framework: A Review
TL;DR: A specifically designed uniform linear array structure with associated CS-based underdetermined DOA estimation is presented to exploit the difference co-array concept in the spatio-spectral domain, leading to a significant increase in degrees of freedom.
References
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Journal ArticleDOI
Multiple emitter location and signal parameter estimation
TL;DR: In this article, a description of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength.
Journal ArticleDOI
An Introduction To Compressive Sampling
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Journal ArticleDOI
ESPRIT-estimation of signal parameters via rotational invariance techniques
R. Roy,Thomas Kailath +1 more
TL;DR: Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise.
Journal ArticleDOI
Sparse bayesian learning and the relevance vector machine
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Journal ArticleDOI
Compressive Sensing [Lecture Notes]
TL;DR: This lecture note presents a new method to capture and represent compressible signals at a rate significantly below the Nyquist rate, called compressive sensing, which employs nonadaptive linear projections that preserve the structure of the signal.
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ESPRIT-estimation of signal parameters via rotational invariance techniques
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