Journal ArticleDOI
Parametric sparse representation method for ISAR imaging of rotating targets
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TLDR
Based on the linear relationship between the chirp rate of cross-range inverse synthetic aperture radar (ISAR) signal and the slant range, a parametric sparse representation method is proposed for ISAR imaging of rotating targets.Abstract:
Based on the linear relationship between the chirp rate of cross-range inverse synthetic aperture radar (ISAR) signal and the slant range, a parametric sparse representation method is proposed for ISAR imaging of rotating targets. The ISAR echo is formulated as a parametric joint-sparse signal and the chirp rates at all range bins are estimated by maximizing the contrast of sparse ISAR image. Comparing with homologous algorithms, the computational complexity of the proposed method is significantly reduced.read more
Citations
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Micro-Doppler Parameter Estimation via Parametric Sparse Representation and Pruned Orthogonal Matching Pursuit
Gang Li,Pramod K. Varshney +1 more
TL;DR: A novel pruned orthogonal matching pursuit (POMP) algorithm is proposed, in which the pruning operation is embedded into the iterative process of the Orthogonal Matching Pursuit algorithm.
Journal ArticleDOI
Autofocusing for Sparse Aperture ISAR Imaging Based on Joint Constraint of Sparsity and Minimum Entropy
TL;DR: This paper combines Laplace approximation-based variational Bayesian inference with the Laplacian scale mixture prior with the minimum entropy criteria to develop a novel autofocusing algorithm for sparse aperture ISAR (SA-ISAR) imaging.
Journal ArticleDOI
High-Resolution Inverse Synthetic Aperture Radar Imaging and Scaling With Sparse Aperture
TL;DR: A novel algorithm for high-resolution ISAR imaging and scaling from SA data is presented, which effectively incorporates the translational motion phase error and MTRC corrections.
Journal ArticleDOI
3D Geometry and Motion Estimations of Maneuvering Targets for Interferometric ISAR With Sparse Aperture
TL;DR: A joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction in ISAR imaging of maneuvering targets from sparse aperture (SA) data.
Journal ArticleDOI
The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
TL;DR: A comprehensive survey is made of recent progress on statistical sparsity based techniques for various radar imagery applications.
References
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Journal ArticleDOI
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
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.
Journal ArticleDOI
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: If the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program.
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Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: In this article, it was shown that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal $f \in {\cal F}$ decay like a power-law, then it is possible to reconstruct $f$ to within very high accuracy from a small number of random measurements.