| 引用本文: | 邱棚,姚旭日,李鸣谦,等.Volterra级数模型的非线性压缩测量辨识算法.[J].国防科技大学学报,2020,42(1):125-132.[点击复制] |
| QIU Peng,YAO Xuri,LI Mingqian,et al.Nonlinear compressed measurement identification based on Volterra series[J].Journal of National University of Defense Technology,2020,42(1):125-132[点击复制] |
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| Volterra级数模型的非线性压缩测量辨识算法 |
| 邱棚1,2,姚旭日1,李鸣谦1,2,翟光杰1 |
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(1. 中国科学院国家空间科学中心, 北京 100190;2. 中国科学院大学, 北京 100049)
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| 摘要: |
| 针对非线性系统的辨识问题,提出了非线性压缩测量辨识算法,且推导出了一种符合压缩感知测量准则的测量模型。相比递归最小二乘法,该方法极大地减少了所需的测量数,使得高阶Volterra级数辨识成为可能。此外,还分析了实际应用中的各项因素对辨识准确性的影响,如信号稀疏度、测量噪声、测量矩阵形式等。 |
| 关键词: 系统辨识 非线性系统 压缩感知 Volterra级数 正交匹配追踪 |
| DOI：10.11887/j.cn.202001017 |
| 投稿日期：2018-10-01 |
| 基金项目:国家自然科学基金资助项目(61605218);中国科学院国防创新基金资助项目(CXJJ-17S023) |
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| Nonlinear compressed measurement identification based on Volterra series |
| QIU Peng1,2, YAO Xuri1, LI Mingqian1,2, ZHAI Guangjie1 |
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(1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China;2. University of Chinese Academy of Sciences, Beijing 100049, China)
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| Abstract: |
| For the identification problem of nonlinear systems, the accuracy and stability of the nonlinear compression measurement identification algorithm were proved in the simulation experiment, and the complete signal was obtained accurately only by using constant multiple measurement times of the signal sparsity. Compared with the least square method, the proposed algorithm has greatly reduced the needed measurements, therefore, it is possible for the identification of high-order Volterra series. Furthermore, the influence of all factors on the accuracy of system identification was analyzed, such as signal sparsity, measurement noise, measurement matrix form, etc. |
| Keywords: system identification nonlinear system compressed sensing Volterra series orthogonal matching pursuit |
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