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The Maximum Correntropy Criterion-Based Robust Hierarchical Estimation Algorithm for Linear Parameter-Varying Systems with Non-Gaussian Noise

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Abstract

The traditional identification algorithms are generally based on the quadratic criterion functions and perform well under the interference of Gaussian noise. However, their performances become worse when non-Gaussian impulsive noise is encountered in practical situations. By introducing the maximum correntropy criterion and utilizing its robustness against outliers, this paper develops a robust recursive algorithm for the linear parameter-varying system with the non-Gaussian noise. The algorithm is not only simple and easy to implement, but also can resist the negative influence of the non-Gaussian noise. Moreover, a maximum correntropy criterion-based robust hierarchical algorithm is presented to increase the calculative efficiency. Three simulation examples are provided to demonstrate the validity of the proposed algorithms.

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Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Acknowledgements

This work was supported by the Key Research Project of Henan Higher Education Institutions (No. 21A413006), Nanhu Scholars Program for Young Scholars of XYNU (No. 2017B67) and Graduate Research and Innovation Fund of Xinyang Normal University (No. 2021KYJJ44).

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

    Qinzhi Li & Xuehai Wang

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  1. Qinzhi Li
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Li, Q., Wang, X. The Maximum Correntropy Criterion-Based Robust Hierarchical Estimation Algorithm for Linear Parameter-Varying Systems with Non-Gaussian Noise. Circuits Syst Signal Process 41, 7117–7144 (2022). https://doi.org/10.1007/s00034-022-02116-1

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