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Open AccessArticle

Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models

by 1, 2 and 1,*
1
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
2
School of Internet of Things Technology, Wuxi Vocational Institute of Commerce, Wuxi 214153, China
*
Author to whom correspondence should be addressed.
Algorithms 2017, 10(4), 118; https://doi.org/10.3390/a10040118
Received: 15 August 2017 / Revised: 7 October 2017 / Accepted: 11 October 2017 / Published: 14 October 2017
This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI) algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models. View Full-Text
Keywords: signal processing; parameter estimation; moving data window; gradient search; Newton search signal processing; parameter estimation; moving data window; gradient search; Newton search
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Liu, S.; Xu, L.; Ding, F. Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models. Algorithms 2017, 10, 118.

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