Results 21 to 30 of about 3,115 (252)
Perturbation Analysis of Orthogonal Matching Pursuit [PDF]
IEEE Transactions on Signal Processing, 2013 29 ...
Jie Ding, Laming Chen, Yuantao Gu
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Journal of Low Frequency Noise, Vibration and Active Control, 2021 This paper proposes a direction of arrival estimation based on sparse signal reconstruction in the presence of alpha noise by the off-grid orthogonal matching pursuit algorithm.
LongKai Liang +3 more
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Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning
Sensors, 2022 A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields.
Yuankun Wei, Hua Yan, Yinggang Zhou
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Efficient Distributed Multi-Task Schemes for mmWave MIMO Channel Estimation
IEEE Access, 2022 In this study, the problem of sparse channel estimation is investigated with the employment of a fully distributed approach. We exploit the spatially joint sparsity structure of the involved channels to formulate the channel estimation problem in the ...
Maria Trigka +2 more
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Rapid Estimation of Orthogonal Matching Pursuit Representation [PDF]
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020 Orthogonal Matching Pursuit (OMP) has proven itself to be a significant algorithm in image and signal processing domain in the last decade to estimate sparse representations in dictionary learning. Over the years, efforts to speed up the OMP algorithm for the same accuracy has been through variants like generalized OMP (g-OMP) and fast OMP (f-OMP). All
Ayan Chatterjee, Peter W. T. Yuen
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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
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A Low-complexity Sparse Channel Estimation Algorithm [PDF]
Jisuanji gongcheng, 2016 Traditional Compressed Sensing(CS)based channel estimation methods are difficult to implement due to their high computational complexity.To solve this problem,Generalized Orthogonal Matching Pursuit(GOMP)algorithm is used for channel estimation,which ...
FAN Xinyue,SU Yantao,ZHOU Fei
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Efficiency of Orthogonal Matching Pursuit for Group Sparse Recovery
Axioms, 2023 We propose the Group Orthogonal Matching Pursuit (GOMP) algorithm to recover group sparse signals from noisy measurements. Under the group restricted isometry property (GRIP), we prove the instance optimality of the GOMP algorithm for any decomposable ...
Chunfang Shao +3 more
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A reduced-complexity compressed sensing channel estimation for underwater acoustic channel
Dianxin kexue, 2021 Aiming at the sparse characteristics of underwater acoustic channels for shallow seas, a reduced-complexity look-ahead backtracking orthogonal matching pursuit (RC-LABOMP) channel estimation algorithm was proposed.Firstly, two types of support sets of ...
Xuan YU, Xuan GENG
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Xibei Gongye Daxue Xuebao, 2020 Efficient localization of multiple targets is one of the basic technical problems in wireless sensor networks (WSN). The traditional sparse representation method based on greedy class is not efficient in multi-target positioning.
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