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Complex Total Least Mean M-Estimate Adaptive Algorithm for Noisy Input and Impulsive Noise

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Abstract

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis.

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The data that support the findings of this study are available from the corresponding author on request.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant: 62171388, 61871461, 61571374), and Fundamental Research Funds for the Central Universities (Grant: 2682021ZTPY091).

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Authors and Affiliations

  1. The Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, and the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Zian Cao, Haiquan Zhao, Yalin Liu & Yida Chen

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  1. Zian Cao
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  2. Haiquan Zhao
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  3. Yalin Liu
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  4. Yida Chen
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Correspondence to Haiquan Zhao.

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Cao, Z., Zhao, H., Liu, Y. et al. Complex Total Least Mean M-Estimate Adaptive Algorithm for Noisy Input and Impulsive Noise. Circuits Syst Signal Process 43, 994–1006 (2024). https://doi.org/10.1007/s00034-023-02492-2

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