| 一种改进的变步长自适应匹配追踪算法 |
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| 引用本文： | 李超,王沛,王传正. 一种改进的变步长自适应匹配追踪算法[J]. 上海师范大学学报(自然科学版), 2017, 46(2): 213-217 |
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| 作者姓名： | 李超 王沛 王传正 |
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| 作者单位： | 上海师范大学 信息与机电工程学院, 上海 200234,上海师范大学 信息与机电工程学院, 上海 200234,上海师范大学 信息与机电工程学院, 上海 200234 |
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| 摘 要： | 压缩感知是利用信号的稀疏性和可压缩性进行信号处理的新理论.针对压缩感知中信号稀疏度未知的问题,提出了一种改进的变步长自适应匹配追踪(MVss AMP)算法.该算法通过计算余量与测量矩阵的相关性,自适应地选择候选集原子,并且通过可变步长更新支撑集,实现信号的精确重建.该算法通过设置一个参数来控制步长变化.仿真结果表明:该算法在误差范围内实现了信号精确重建,并且重建性能优于其他同类算法.
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| 关 键 词： | 压缩感知 信号处理 匹配追踪 自适应 |
| 收稿时间： | 2015-09-17 |
An improved variable step size adaptive matching pursuit algorithm |
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| Li Chao,Wang Pei and Wang Chuanzheng. An improved variable step size adaptive matching pursuit algorithm[J]. Journal of Shanghai Normal University(Natural Sciences), 2017, 46(2): 213-217 |
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| Authors: | Li Chao Wang Pei Wang Chuanzheng |
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| Affiliation: | College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China,College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China and College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China |
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| Abstract: | Compressed sensing is a new theory of using signal sparsity and compressibility for signal processing.To solve the problem that the signal sparse is unknown in compressed sensing,an modified variable step size adaptive matching pursuit(MVssAMP) algorithm is put forward in this paper.This algorithm achieves the accurate reconstruction of the signal by calculating the correlation between the residual and the measurement matrix,then adaptively selecting candidate sets of atoms,and updating the support set via variable step size.The algorithm controls the step size change by setting a parameter.The simulation results show that the algorithm can achieve the accurate reconstruction of the signal within its error range,and the reconstruction performance is superior to other similar algorithms. |
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| Keywords: | compressed sensing signal processing matching pursuit self-adaption |
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