| 小波变换的稀疏最优化信号趋势项提取方法 |
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| 引用本文： | 马子骥,钟广超,刘宏立,李艳福. 小波变换的稀疏最优化信号趋势项提取方法[J]. 传感器与微系统, 2017, 36(1). DOI: 10.13873/J.1000-9787(2017)01-0027-04 |
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| 作者姓名： | 马子骥 钟广超 刘宏立 李艳福 |
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| 作者单位： | 湖南大学电气与信息工程学院,湖南长沙,410082 |
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| 基金项目： | 中央国有资本经营预算项目，中央高校基本科研项目，国家自然科学基金资助项目，湖南省科技计划资助项目，中国博士后科研基金资助项目，湖南省科技计划资助重点项目 |
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| 摘 要： | 针对非线性信号的趋势项,提出一种基于小波变换的稀疏最优化方法(WT-SO).该方法通过设置两个边界约束条件,求取l1范数稀疏最优解重构信号趋势项.仿真信号与实测信号处理结果表明:该方法依据信号自身的特性来定义趋势项,不需要对信号作任何假设,比传统的提取趋势项的方法具有更高的精度和可靠性,且在各种噪声环境下均具有良好的鲁棒性.
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| 关 键 词： | 信号趋势 小波变换 稀疏最优化 边界约束 l1范数稀疏最优解 |
Sparse optimization methods for signal trend term extraction based on wavelet transform |
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| MA Zi-ji,ZHONG Guang-chao,LIU Hong-li,LI Yan-fu. Sparse optimization methods for signal trend term extraction based on wavelet transform[J]. Transducer and Microsystem Technology, 2017, 36(1). DOI: 10.13873/J.1000-9787(2017)01-0027-04 |
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| Authors: | MA Zi-ji ZHONG Guang-chao LIU Hong-li LI Yan-fu |
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| Abstract: | In view of the trend term of nonlinear signal,propose a sparse optimization method based on wavelet transform.The method calculates the l1 norm sparse optimal solution to reconstruct the trend of signal by setting two boundary constraints.Simulation signal and measured signal processing result show that the method define trend item according to its self characteristic,don't need to make any assumptions and it is fully data-driven,and has a good robustness in different noise intensity environment,the method has higher precision and reliability than the traditional method. |
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| Keywords: | signal trend wavelet transform sparse optimization boundary constraints l1 norm sparse optimal solution |
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