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Hierarchical Principle-Based Iterative Parameter Estimation Algorithm for Dual-Frequency Signals

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Abstract

In this paper, we consider the parameter estimation problem of dual-frequency signals disturbed by stochastic noise. The signal model is a highly nonlinear function with respect to the frequencies and phases, and the gradient method cannot obtain the accurate parameter estimates. Based on the Newton search, we derive an iterative algorithm for estimating all parameters, including the unknown amplitudes, frequencies, and phases. Furthermore, by using the parameter decomposition, a hierarchical least squares and gradient-based iterative algorithm is proposed for improving the computational efficiency. A gradient-based iterative algorithm is given for comparisons. The numerical examples are provided to demonstrate the validity of the proposed algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61873111) and the 111 Project (B12018).

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Siyu Liu, Feng Ding & Ling Xu

  2. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, People’s Republic of China

    Feng Ding

  3. Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Feng Ding & Tasawar Hayat

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  1. Siyu Liu
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  2. Feng Ding
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  3. Ling Xu
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Correspondence to Feng Ding.

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Liu, S., Ding, F., Xu, L. et al. Hierarchical Principle-Based Iterative Parameter Estimation Algorithm for Dual-Frequency Signals. Circuits Syst Signal Process 38, 3251–3268 (2019). https://doi.org/10.1007/s00034-018-1015-1

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