Optimal Nonuniform Sampling for System Identification on Sparsely Sampled Data

In this paper the problem of optimal Nonuniform sampling （NUS） is addressed for the purpose of sparsely sampled data system identification. Given a set of uniformly sampled data, its spectral information is available in the range limited by Nyquist rate, and results in alias out of the range. This c...

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Bibliographic Details
Published in:电子学报：英文版 2012-04, Vol.21 (2), p.292-298
Main Author: NI Boyi XIAO Deyun
Format: Article
Language:English
Subjects:
取样信号
奈奎斯特速率
数据采样
新加坡国立大学
稀疏
系统辨识
采样数据系统
非均匀采样
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Summary:In this paper the problem of optimal Nonuniform sampling （NUS） is addressed for the purpose of sparsely sampled data system identification. Given a set of uniformly sampled data, its spectral information is available in the range limited by Nyquist rate, and results in alias out of the range. This cannot meet the ＂informa- tive enough＂ condition, which is one indispensable prereq- uisite for system identifiability. Nevertheless, deliberate NUS pattern with certain random distributions can keep the alias-free feature of sampled signals and recover wider spectrum of the original signal~ so that the identifiability is still guaranteed. In the case that no ideal alias-free sig- nal is available, a criterion of alias suppression is founded and the optimal sampling is proposed to give an effective estimation of such systems with sparse samples. Simula- tion results shows the practicality and effectiveness of the proposed optimal sampling method, and how the identified model accuracy is affected by NUS.
ISSN:1022-4653