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Bias Correction-Based Recursive Estimation for Dual-Rate Output-Error Systems with Sampling Noise

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Abstract

This paper develops a bias correction-based recursive estimation algorithm for dual-rate output-error systems. The system output is subjected to both output noise and sampling noise. Using the polynomial transformation technique, the dual-rate output-error system is converted into an identification model where the sampled data can be directly applied. The noise variances of output noise and sampling noise are estimated by solving a nonlinear equation, which can minimize the estimation errors of noise variances. The simulation examples demonstrate that the proposed algorithm has higher parameter estimation accuracy in contrast to the auxiliary model-based recursive least-squares algorithm.

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Acknowledgements

This work was supported by the Science and Technology Project of Henan Province (No. 202102210297), the Science and Technology Research Key Project of the Education Department of Henan Province (Nos. 20A110031, 20A430023, 20B130002) and Nanhu Scholars Program for Young Scholars of XYNU.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xinyang Normal University, Xinyang, 464000, People’s Republic of China

    Xuehai Wang

  2. Institute of Education Science, Xinyang Normal University, Xinyang, 464000, People’s Republic of China

    Fang Zhu

  3. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266061, People’s Republic of China

    Feng Ding

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  1. Xuehai Wang
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Correspondence to Xuehai Wang.

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Wang, X., Zhu, F. & Ding, F. Bias Correction-Based Recursive Estimation for Dual-Rate Output-Error Systems with Sampling Noise. Circuits Syst Signal Process 39, 4297–4319 (2020). https://doi.org/10.1007/s00034-020-01378-x

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