| 基于压缩感知的稀疏度自适应匹配追踪改进算法 |
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| 引用本文： | 吕伟杰,孟博,张飞. 基于压缩感知的稀疏度自适应匹配追踪改进算法[J]. 控制与决策, 2018, 33(9): 1657-1661 |
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| 作者姓名： | 吕伟杰 孟博 张飞 |
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| 作者单位： | 天津大学电气自动化与信息工程学院，天津300072,天津大学电气自动化与信息工程学院，天津300072,天津大学电气自动化与信息工程学院，天津300072 |
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| 摘 要： | 针对稀疏度自适应匹配追踪(Sparsity adaptive matching pursuit,SAMP)算法存在预选原子过多、重构时间长、步长的选择固定等缺点,提出一种稀疏度自适应匹配追踪改进算法.该算法将稀疏度预先设定值与稀疏度估计过量判据相结合进行真实稀疏度快速估计,通过模糊阈值的方法提高候选原子的精确度,采用原子相关阈值改善迭代停止条件,最终实现信号的精确重构.仿真实验表明,改进算法重构质量较好于SAMP算法,重构速率显著提高.
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| 关 键 词： | 压缩感知 稀疏度估计 模糊阈值 重构信号 |
Modified sparsity adaptive matching pursuit algorithm based on compressive sensing |
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| LYU Wei-jie,MENG Bo and ZHANG Fei. Modified sparsity adaptive matching pursuit algorithm based on compressive sensing[J]. Control and Decision, 2018, 33(9): 1657-1661 |
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| Authors: | LYU Wei-jie MENG Bo ZHANG Fei |
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| Affiliation: | School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China,School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China and School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China |
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| Abstract: | Due to the fact that the sparsity adaptive matching pursuit(SAMP) algorithm has the disadvantages of overmuch candidate atoms, overlong reconstruction time and fixed selection of pace, the paper proposes a modification algorithm based on SAMP. Firstly, by combining the sparse degree of preset values with excessive estimate criterion, this algorithm conducts rapid estimation of real sparse degree. Then the fuzzy threshold method is used to improve the accuracy of candidate atoms. Finally, the iteration stop condition is improved through utilizing atoms relative threshold so as to realize accurate reconstruction of signals. The simulation experiments verify that, compared with the SAMP algorithm, the proposed algorithm not only has higher speed of signal reconstruction, but also improves the quality of reconstruction. |
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