Nonlinear compressed measurement identification based on Volterra series

被引：1
作者：
Qiu P. [1 ,2 ]
Yao X. [1 ]
Li M. [1 ,2 ]
Zhai G. [1 ]
机构：
[1] National Space Science Center, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2020年 / 42卷 / 01期
关键词：
Compressed sensing; Nonlinear system; Orthogonal matching pursuit; System identification; Volterra series;
D O I：
10.11887/j.cn.202001017
中图分类号：
学科分类号：
摘要：
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. © 2020, NUDT Press. All right reserved.
引用
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页码：125 / 132
页数：7
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