Results 11 to 20 of about 277,175 (273)
Sparse Recovery Using Sparse Matrices [PDF]
Proceedings of the IEEE, 2010 In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals.
Gilbert, Anna, Indyk, Piotr
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ISAR Imaging Algorithm Based on Iterative Weighted L2/L1 Norm Block Sparse Signal Recovery [PDF]
Jisuanji gongcheng, 2018 In order to realize fast and high resolution Inverse Synthetic Aperture Radar(ISAR)imaging,an iterative weighted L2/L1 norm block sparse recovery algorithm for ISAR imaging is proposed based on the target’s intrinsic block sparse structure information ...
FENG Junjie,ZHANG Gong
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Remote Sensing, 2022 Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments.
Yi Ding +4 more
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Jump-Sparse and Sparse Recovery Using Potts Functionals [PDF]
IEEE Transactions on Signal Processing, 2014 We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and provide relations to sparsity problems.
Martin Storath +2 more
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ISAR Imaging of Non-Stationary Moving Target Based on Parameter Estimation and Sparse Decomposition
Remote Sensing, 2023 This paper studies the inverse synthetic aperture radar imaging problem for a non-stationary moving target and proposes a non-search imaging method based on parameter estimation and sparse decomposition.
Can Liu +3 more
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Sparse Recovery With Graph Constraints [PDF]
IEEE Transactions on Information Theory, 2015 Sparse recovery can recover sparse signals from a set of underdetermined linear measurements. Motivated by the need to monitor large-scale networks from a limited number of measurements, this paper addresses the problem of recovering sparse signals in the presence of network topological constraints.
null Meng Wang +3 more
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An Overview on Sparse Recovery-based STAP
Leida xuebao, 2014 This paper gives a brief review on the Sparse-Recovery (SR)-based Space-Time Adaptive Processing (STAP) technique. First, the motivation for introducing sparse recovery into STAP is presented.
Ma Ze-qiang +3 more
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Approximate sparse recovery [PDF]
Proceedings of the forty-second ACM symposium on Theory of computing, 2010 An approximate sparse recovery system consists of parameters $k,N$, an $m$-by-$N$ measurement matrix, $ $, and a decoding algorithm, $\mathcal{D}$. Given a vector, $x$, the system approximates $x$ by $\widehat x =\mathcal{D}( x)$, which must satisfy $\| \widehat x - x\|_2\le C \|x - x_k\|_2$, where $x_k$ denotes the optimal $k$-term approximation to $
Gilbert, Anna C. +3 more
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Mathematics, 2019 Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory.
Dingfei Jin +3 more
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High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error
Leida xuebao, 2021 The Orbital Angular Momentum (OAM)-based vortex radar has drawn increasing attention because of its potential for high-resolution imaging. The vortex radar high resolution imaging with limited OAM modes is commonly solved by sparse recovery, in which the
Haiyou QU +3 more
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