Results 31 to 40 of about 141,696 (278)
A Scale-Invariant Approach for Sparse Signal Recovery [PDF]
SIAM Journal on Scientific Computing, 2019 In this paper, we study the ratio of the $L_1 $ and $L_2 $ norms, denoted as $L_1/L_2$, to promote sparsity. Due to the non-convexity and non-linearity, there has been little attention to this scale-invariant model. Compared to popular models in the literature such as the $L_p$ model for $p\in(0,1)$ and the transformed $L_1$ (TL1), this ratio model is ...
Rahimi, Yaghoub +3 more
openaire +3 more sources
Sparse signal and image recovery from Compressive Samples [PDF]
, 2007 In this paper we present an introduction to Compressive Sampling (CS), an emerging model-based framework for data acquisition and signal recovery based on the premise that a signal having a sparse representation in one basis can be reconstructed from
Braun, Nathaniel +2 more
core +1 more source
Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification [PDF]
Journal of Electrical and Computer Engineering Innovations, 2021 Background and Objectives: Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can
Z. Habibi +2 more
doaj +1 more source
Sequential Compressed Sensing [PDF]
, 2009 Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sensing literature have focused on characterizing the achievable performance ...
Malioutov, Dmitry +2 more
core +1 more source
Uncertainty Relations and Sparse Signal Recovery [PDF]
, 2021 This chapter provides a principled introduction to uncertainty relations underlying sparse signal recovery. We start with the seminal work by Donoho and Stark, 1989, which defines uncertainty relations as upper bounds on the operator norm of the band-limitation operator followed by the time-limitation operator, generalize this theory to arbitrary pairs
Erwin Riegler, Helmut Bökei
openaire +1 more source
ISAR Imaging Algorithm for Parameter Iterative Minimization Sparse Signal Recovery [PDF]
Jisuanji gongcheng, 2018 In order to obtain the robustness Inverse Synthetic Aperture Radar(ISAR) image,an iterative minimization Bayesian learning sparse signal recovery algorithm is proposed.Firstly,ISAR imaging is established,and the imaging problem is converted to sparse ...
FENG Junjie,ZHANG Gong
doaj +1 more source
Restricted p-Isometry Properties of Partially Sparse Signal Recovery
Discrete Dynamics in Nature and Society, 2013 By generalizing the restricted p-isometry property to the partially sparse signal recovery problem, we give a sufficient condition for exactly recovering partially sparse signal via the partial lp minimization (truncated lp minimization) problem with p ...
Haini Bi, Lingchen Kong, Naihua Xiu
doaj +1 more source
Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
IEEE Access, 2022 Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of ...
Irfan Ahmed +3 more
doaj +1 more source
Sparse Signal Recovery for Direction-of-Arrival Estimation Based on Source Signal Subspace
Journal of Applied Mathematics, 2014 After establishing the sparse representation of the source signal subspace, we propose a new method to estimate the direction of arrival (DOA) by solving an ℓ1-norm minimization for sparse signal recovery of the source powers.
Bo Lin, Jiying Liu, Meihua Xie, Jubo Zhu
doaj +1 more source