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Iterative Parameter Estimation for Signal Models Based on Measured Data

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Abstract

This paper studies the modeling of multi-frequency signals based on measured data. With the use of the hierarchical identification principle and the iterative search, several iterative parameter estimation algorithms are derived for the signal models with the known frequencies and the unknown frequencies. For the multi-frequency signals, the hierarchical estimation algorithms are derived by means of parameter decomposition. Through the decomposition, the original optimization problem is transformed into the combination of the nonlinear optimization and the linear optimization problems. The simulation results show that the proposed hierarchical algorithms have better performance than the overall estimation algorithms without parameter decomposition.

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Acknowledgements

This work was supported by the Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 16KJB120007) and sponsored by the Qing Lan Project of Jiangsu Province, the National Natural Science Foundation of China (No. 61293194, 61773182) and the Natural Science Foundation of Jiangsu Province (No. BK20160162).

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Authors and Affiliations

  1. School of Internet of Things Technology, Wuxi Vocational Institute of Commerce, Wuxi, 214153, People’s Republic of China

    Ling Xu

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Ling Xu & Feng Ding

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  1. Ling Xu
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  2. Feng Ding
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Correspondence to Feng Ding.

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Xu, L., Ding, F. Iterative Parameter Estimation for Signal Models Based on Measured Data. Circuits Syst Signal Process 37, 3046–3069 (2018). https://doi.org/10.1007/s00034-017-0705-4

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