Journal ArticleDOI
Bayesian compressive sensing in synthetic aperture radar imaging
TLDR
The results indicate that the Bayesian formalism can provide a sharp and sparse image absence of side-lobes, which is the common problem in conventional imaging methods and has fewer artifacts compared with the previous version of CS-based methods.Abstract:
To achieve high-resolution two dimension images, synthetic aperture radar (SAR) with ultra wide-band faces considerably technical challenges such as long data collection time, huge amount of data storage and high hardware complexity In these years, several imaging modalities based on compressive sensing (CS) have been proposed which can provide high-resolution images using significantly reduced number of samples However, the CS-based methods are sensitive to noise and clutter In this study, a new imaging modality based on Bayesian compressive sensing (BCS) is proposed along with a novel compressed sampling scheme Clutter, which the previous CS-based methods not considered, is also included in this study This new imaging scheme requires minor change to traditional system and allows both range and azimuth compressed sampling Also, the Bayesian formalism accounts for additive noise encountered in the compressed measurement process Experiments are carried out with noisy and cluttered imaging scenes to verify the new imaging scheme The results indicate that the Bayesian formalism can provide a sharp and sparse image absence of side-lobes, which is the common problem in conventional imaging methods and has fewer artifacts compared with the previous version of CS-based methodsread 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.
Journal ArticleDOI
Reliable Diagnosis of Large Linear Arrays—A Bayesian Compressive Sensing Approach
TL;DR: The arising Bayesian compressive sensing (BCS) approach is numerically validated through a set of representative examples aimed at providing suitable user's guidelines as well as some insights on the method features and potentialities.
Journal ArticleDOI
An Autofocus Technique for High-Resolution Inverse Synthetic Aperture Radar Imagery
TL;DR: Experimental results based on synthetic and practical data have demonstrated that the proposed algorithm has a desirable denoising capability and can produce a relatively well-focused image of the target, particularly in low signal-to-noise ratio and high undersampling ratio scenarios, compared with other recently reported methods.
Journal ArticleDOI
Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene
TL;DR: A novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework that can achieve substantial improvements in the scenarios of limited measurements and low signal-to-noise ratio compared with other reported algorithms for ISAR imaging problems.
Journal ArticleDOI
Sparse Representation-Based ISAR Imaging Using Markov Random Fields
TL;DR: A novel sparse representation (SR)-based inverse synthetic aperture radar (ISAR) imaging algorithm is proposed by leveraging the Markov random fields (MRF), and variational Bayes expectation-maximization method is used to simultaneously approximate the posterior of the hidden variables and estimate the model parameters of the MRF.
References
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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.
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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
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
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.