Results 11 to 20 of about 981,446 (269)
Structured Bayesian Orthogonal Matching Pursuit [PDF]
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012 Taking advantage of the structures inherent in many sparse decompositions constitutes a promising research axis. In this paper, we address this problem from a Bayesian point of view. We exploit a Boltzmann machine, allowing to take a large variety of structures into account, and focus on the resolution of a joint maximum a posteriori problem.
Drémeau, Angélique +2 more
core +10 more sources
Perturbed Orthogonal Matching Pursuit [PDF]
IEEE Transactions on Signal Processing, 2013 Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed with an underdetermined linear measurement model. However, in reality there is a mismatch between the assumed and the actual bases due to factors such as discretization of the parameter space defining basis components, sampling jitter in A/D ...
Oguzhan Teke +2 more
openaire +7 more sources
An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks
International Journal of Distributed Sensor Networks, 2022 Aiming at the problems of low data reconstruction accuracy in wireless sensor networks and users unable to receive accurate original signals, improvements are made on the basis of the stagewise orthogonal matching pursuit algorithm, combined with ...
Xinmiao Lu +4 more
doaj +2 more sources
Sparse Recovery With Orthogonal Matching Pursuit Under RIP [PDF]
IEEE Transactions on Information Theory, 2011 This paper presents a new analysis for the orthogonal matching pursuit (OMP) algorithm. It is shown that if the restricted isometry property (RIP) is satisfied at sparsity level $O(\bar{k})$, then OMP can recover a $\bar{k}$-sparse signal in 2-norm. For compressed sensing applications, this result implies that in order to uniformly recover a $\bar{k ...
Tong Zhang
exaly +5 more sources
On The Exact Recovery Condition of Simultaneous Orthogonal Matching Pursuit [PDF]
IEEE Signal Processing Letters, 2016 Several exact recovery criteria (ERC) ensuring that orthogonal matching pursuit (OMP) identifies the correct support of sparse signals have been developed in the last few years. These ERC rely on the restricted isometry property (RIP), the associated restricted isometry constant (RIC) and sometimes the restricted orthogonality constant (ROC).
François Horlin +2 more
exaly +6 more sources
Comparison Of Orthogonal Matching Pursuit Implementations [PDF]
, 2012 Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Bob L. T. Sturm +1 more
openaire +4 more sources
Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
Journal of Marine Science and Engineering, 2023 Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time–frequency space (OTFS) modulation signals.
Yuzhi Zhang +4 more
doaj +1 more source
Hybrid Precoding Algorithm Based on Discrete Orthogonal Matching Pursuit for Millimeter-Wave Relay Networks [PDF]
Jisuanji gongcheng, 2020 In order to improve the beamforming gain of massive Multiple-Input Multiple-Output(MIMO) relay system and reduce the hardware cost of phase shifters and radio frequency links in hybrid precoding architecture,this paper proposes a hybrid precoding ...
DING Qingfeng, GAO Xinpeng, DENG Yuqian
doaj +1 more source
Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction
IEEE Open Journal of Signal Processing, 2023 The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruction or approximation. It acts as a driving force for the development of several other greedy methods for sparse data reconstruction, and it also plays a ...
Yun-Bin Zhao, Zhi-Quan Luo
doaj +1 more source
Online Orthogonal Matching Pursuit
CoRR, 2020 Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the sample complexity, with little or no consideration of the computation resources required.
Saad, El Mehdi +2 more
openaire +3 more sources