NDLI: Support vector regression -based robust frequency estimation algorithm by instantaneous phase

Content Provider	IET Digital Library
Author	Liu, Xueqian Yu, Hongyi
Abstract	There are two important factors which impact the performance of phase-based frequency estimation algorithms remarkably: the approximations of noise phase model and imperfections in phase unwrapping process. Support vector regression (SVR) exits excellent capabilities for learning unknown data and forecasting the future ones, especially under the small sample condition. Therefore, the authors introduce it to predict the variation trend of instantaneous phase and unwrap phases efficiently. Even though with that being the case, errors still exist in phase unwrapping process because of its ambiguous phase characteristic. Furthermore, a SVR-based frequency estimation algorithm is proposed and makes it immune to these error phases by means of setting the SVR's parameters properly. The results show that, compared with other phased-based algorithms, not only does the proposed one maintain a wide estimation range and quality capabilities at low frequencies, but also improves accuracy at high frequencies and decreases the impact with the initial phase. The proposed algorithm is fit for not only linear phase signal but also polynomial phase signal, under both the Gaussian and non-Gaussian condition.
Starting Page	250
Ending Page	257
Page Count	8
ISSN	17518628
Volume Number	8
e-ISSN	17518636
Issue Number	Issue 2, Jan (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/8/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2013.0589
Journal	IET Communications
Publisher Date	2014-01-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Ambiguous Phase Characteristic Frequency Estimation Instantaneous Phase Learning in AI Learning Unknown Data Linear Phase Signal Noise Phase Model NonGaussian Condition Phase Unwrapping Process Phase-based Frequency Estimation Algorithm Polynomial Phase Signal Regression Analysis Signal Processing Signal Processing And Detection Signal Processing Theory Statistics Support Vector Machine Support Vector Regression -based Robust Frequency Estimation Algorithm SVR-based Frequency Estimation Algorithm Unwrap Phase
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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