Results 21 to 30 of about 72,581 (258)

A note on orthogonal matching pursuit under restricted isometry property

IET Signal Processing, 2022
The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal ...
Xueping Chen, Jianzhong Liu, Xianwen Ding, Hengzhen Huang   +3 more
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Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l₀ Norm and Randomized Coordinate Descent

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, Guang Yang, Zhenghui Li, Haode Liu   +3 more
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Cluster-Sparse Proportionate NLMS Algorithm With the Hybrid Norm Constraint

IEEE Access, 2018
In this paper, an enhanced proportionate normalized least mean square (PNLMS) algorithm with the hybrid l2,0-norm constraint is proposed for block-sparse signal processing.
Yingsong Li, Zhengxiong Jiang, Zhan Jin, Xiao Han, Jingwei Yin   +4 more
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Sparse Analysis Recovery via Iterative Cosupport Detection Estimation

IEEE Access, 2021
Cosparse analysis model (CAM) provides a new signal processing paradigm for recovering cosparse signals with respect to a given analysis operator from the undersampled linear measurements in the context of emerging theory of compressed sensing (CS).
Heping Song, Xiaolong Ren, Yuping Lai, Hongying Meng   +3 more
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A Review of Radar Signal Processing Based on Sparse Recovery

Leida xuebao
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing.
Yinghui QUAN, Yaojun WU, Lining DUAN, Gang XU, Min XUE, Zhixing LIU, Mengdao XING   +6 more
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Sparse Signal Recovery via Exponential Metric Approximation

Tsinghua Science and Technology, 2017
Sparse signal recovery problems are common in parameter estimation, image processing, pattern recognition, and so on. The problem of recovering a sparse signal representation from a signal dictionary might be classified as a linear constraint ℓ0 ...
Jian Pan, Jun Tang, Wei Zhu
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Imaging Method for Co-prime-sampling Space-borne SAR Based on 2D Sparse-signal Reconstruction

Leida xuebao, 2020
Co-prime-sampling space-borne Synthetic Aperture Radar (SAR) replaces the traditional uniform sampling by performing co-prime sampling in azimuth, which effectively alleviates the conflict between spatial resolution and effective swath width, while also
ZHAO Wanwan, WANG Pengbo, MEN Zhirong, LI Chunsheng   +3 more
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Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation

Jixie chuandong, 2020
Sparse representation has a wide range of applications in the field of image processing and audio processing. Applying the sparse representation theory to the field of vibration signal processing can efficiently represent the periodic components of the ...
Xiaoyun Gong, Weiye Zhang, Yongjie Jing, Wenliao Du   +3 more
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RIPless Based Radar Waveform Analysis in Sparse Microwave Imaging

Leida xuebao, 2013
The echo data can be modeled as the product of the Toeplitz matrix and reflectivity of the observed scene. The row of the Toeplitz matrix is the time-shift of the transmitted signal.
Zhao Yao, Zhang Bing-chen, Hong Wen, Wu Yi-rong   +3 more
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2-D Joint Sparse Reconstruction and Micro-Motion Parameter Estimation for Ballistic Target Based on Compressive Sensing

Sensors, 2020
The sparse frequency band (SFB) signal presents a serious challenge to traditional wideband micro-motion curve extraction algorithms. This paper proposes a novel two-dimension (2-D) joint sparse reconstruction and micro-motion parameter estimation (2D ...
Jiaqi Wei, Shuai Shao, Lei Zhang, Hongwei Liu   +3 more
doaj   +1 more source