基于稀疏贝叶斯学习的时域流信号鲁棒动态压缩感知算法
A Robust Dynamic Compressive Sensing Algorithm for Streaming Signals in Time Domain Based on Sparse Bayesian Learning
查看参考文献20篇
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文摘
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块效应和未知且时变的噪声强度会降低时域流信号动态稀疏重构的性能,为解决该问题,本文基于重叠正交变换和稀疏贝叶斯学习框架,提出一种对时域流信号进行动态压缩感知的鲁棒稀疏贝叶斯学习重构算法.该算法在消除块效应的同时,能够处理噪声强度未知且时变情形下的动态稀疏重构问题,相比现有的流信号稀疏贝叶斯学习算法具有更强的抗噪鲁棒性.尽管现有的时域流信号压缩感知的有效算法并不多,但实验表明,本文算法的重构信误比和重构成功率均明显高于现有的基于稀疏贝叶斯学习的流信号重构算法和基于L1-同伦的流信号重构算法,且达到相同的重构成功率所需的观测数目少于另两种算法,计算量和运行效率则与稀疏贝叶斯学习算法相近. |
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其他语种文摘
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Performance of dynamic sparse recovery for streaming signals in time domain will degrade for the existence of blocking artifacts and unknown time-varying noise intensity.To solve the above problems,a robust sparse Bayesian learning algorithm for dynamic compressive sensing of streaming signals in time domain is proposed based on the framework of lapped orthogonal transform and sparse Bayesian learning.In addition to eliminating the blocking artifacts,the proposed algorithm handles dynamic sparse Bayesian learning problems effectively under conditions of unknown time-varying noise intensity,which has better robustness against existing sparse Bayesian learning algorithms for streaming signals.Though there are not many existing effective algorithms for compressed sensing of streaming signals,experiments show that the proposed algorithm has obviously larger reconstruction signal-to-noise ratio and higher success rates for reconstruction than existing recovery algorithms for streaming signals based on sparse Bayesian learning or L1-homotopy;also,the measurement number required for particular success rates is obviously less than that of the other two algorithms,the computation cost and running time is approximately the same with the existing sparse Bayesian learning algorithm. |
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来源
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电子学报
,2020,48(5):990-996 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2020.05.021
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关键词
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块效应
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流信号
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稀疏贝叶斯学习
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动态重构
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地址
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海军航空大学, 信号与信息处理山东省重点实验室, 山东, 烟台, 264001
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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电子技术、通信技术 |
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基金
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国家自然科学基金
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文献收藏号
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CSCD:6770152
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参考文献 共
20
共1页
|
|
1.
Vaswani N. Kalman filtered compressed sensing.
15th IEEE International Conference on Image Processing,2008:893-896
|
CSCD被引
3
次
|
|
|
|
|
2.
Vaswani N. LS-CS-residual (LS-CS):Compressive sensing on least squares residual.
IEEE Transactions on Signal Processing,2010,58(8):4108-4120
|
CSCD被引
9
次
|
|
|
|
|
3.
Wang Y. Exploiting the convex-concave penalty for tracking:A novel dynamic reweighted sparse Bayesian learning algorithm.
2014 IEEE International Conference on Acoustics,Speech and Siganl Processing,2014:3345-3349
|
CSCD被引
1
次
|
|
|
|
|
4.
Donoho D L. Message passing algorithms for compressed sensing.
Proceedings of the National Academy of Sciences of the United States of America,2009,106(45):18914
|
CSCD被引
47
次
|
|
|
|
|
5.
Ziniel J. Tracking and smoothing of time-varying sparse signals via approximate belief propagation.
11th Asilomar Conference on Signals,Systems and Computers,2010:808-812
|
CSCD被引
1
次
|
|
|
|
|
6.
Ziniel J. Dynamic compressive sensing of time-varying signals via approximate message passing.
IEEE Transactions on Signal Processing,2013,61(21):5270-5284
|
CSCD被引
11
次
|
|
|
|
|
7.
Goertz N. Fast Bayesian signal recovery in compressed sensing with partially unknown discrete prior.
Wsa International Itg Workshop on Smart Antennas,2017
|
CSCD被引
1
次
|
|
|
|
|
8.
Li Z. Compressed sensing via dictionary learning and approximate message passing for multimedia internet of things.
IEEE Internet of Things Journal,2016,4(2):505-512
|
CSCD被引
1
次
|
|
|
|
|
9.
Asif M S. Sparse recovery of streaming signals using l1-homotopy.
IEEE Transactions on Signal Processing,2014,62(16):4209-4223
|
CSCD被引
7
次
|
|
|
|
|
10.
Wijewardhana U L. A Bayesian approach for online recovery of streaming signals from compressive measurements.
IEEE Transactions on Signal Processing,2016,65(1):184-199
|
CSCD被引
1
次
|
|
|
|
|
11.
Malvar H S. The LOT:Transform coding without blocking effects.
IEEE Trans on Acoustics,Speech and Signal,1989,37(4):553-559
|
CSCD被引
7
次
|
|
|
|
|
12.
Malvar H S.
Signal Processing with Lapped Transforms,1992
|
CSCD被引
12
次
|
|
|
|
|
13.
Tipping M E. Sparse Bayesian learning and the relevance vector machine.
Journal of Machine Learning Research,2001,1(3):211-244
|
CSCD被引
453
次
|
|
|
|
|
14.
Tipping M E. Fast marginal likelihood maximization for sparse Bayesian models.
Proc Int Workshop AI Statist,2003:3-6
|
CSCD被引
1
次
|
|
|
|
|
15.
Zhang Z.
Sparse signal recovery exploiting spatiotemporal correlation. Theses and Dissertations,2012
|
CSCD被引
1
次
|
|
|
|
|
16.
Ji S. Multi-task compressive sensing.
IEEE Transactions on Signal Processing,2009,57(1):92-106
|
CSCD被引
26
次
|
|
|
|
|
17.
田文飚. 基于盲自适应KLT的蒸发波导压缩感知方法.
电子学报,2018,46(9):22-28
|
CSCD被引
1
次
|
|
|
|
|
18.
田文飚. 三维观测模型下蒸发波导时空态势压缩感知.
电波科学学报,2014(2):207-212
|
CSCD被引
3
次
|
|
|
|
|
19.
成印河. Babin模式特征分析及应用.
海洋科学,2008,32(7)
|
CSCD被引
1
次
|
|
|
|
|
20.
余贵水. 蒸发波导诊断的Babin模型及其敏感性分析.
宇航计测技术,2015(2):53-56
|
CSCD被引
2
次
|
|
|
|
|
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