| Peer-Reviewed

Generalized Weighted Adaptive Time Delay Estimation Algorithm Based on Minimum Average P Norm

Received: 22 September 2021     Accepted: 13 October 2021     Published: 16 October 2021
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Abstract

The time delay estimation algorithm is one of the important factors of sound source localization. The generalized weighted adaptive time delay estimation algorithm is limited by the environmental conditions of signal and noise, and has great limitations in non-Gaussian environments. In order to make the algorithm suitable for non-Gaussian environments, and to retain the advantages of the algorithm in effectively suppressing harmonics, this paper combines the minimum average P norm (LMP) with the generalized weighting function, and proposes a method based on the minimum average P norm. The generalized weighted adaptive time delay estimation algorithm of the number can make the algorithm suitable for non-Gaussian environments, and for the shortcomings of slow iteration speed and large calculation amount for the minimum average P norm, the Sigmoid function is introduced to further improve the parameter selection in the algorithm. MATLAB simulation experiments show that the algorithm in this paper can effectively suppress the existence of harmonics in a non-Gaussian environment, and has strong convergence, high accuracy of time delay estimation, and fast iteration speed. It can be based on time delay estimation in a non-Gaussian environment. The positioning plays a certain role.

Published in Journal of Electrical and Electronic Engineering (Volume 9, Issue 5)
DOI 10.11648/j.jeee.20210905.13
Page(s) 161-169
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

α Stable Distribution, Generalized Weighting Function, Sigmoid Function, LMP Algorithm

References
[1] Liu Wenhong. Detection of evoked potential latency extension based on resilience time delay estimation [J]. Journal of Shanghai Dianji University, 2010, 13 (01): 4-8.
[2] Wang Song. Research and implementation of sound source localization algorithm based on TDOA [D]. Shandong University, 2020.
[3] Carter G C, Nuttall A H, Cable P G The smoothed coherence transform [J]. Proceedings of the IEEE, 1973, 61 (10): 1497-1498.
[4] Tang Juan, HANG Hongyan. Time Delay Estimation Method Based on Quadratic Correlation [J]. Computer Engineering, 2007 (21): 265-267.
[5] Xu Xiaosu, Sun Xiaojun, Zhang Tao, Tong Jinwu. Ultra-short baseline underwater acoustic localization algorithm based on repeated generalized cross-correlation time delay estimation [J]. Journal of Chinese Inertial Technology, 2019, 27 (01): 66-71.
[6] Feintuch, P, Bershad, et al. Time delay estimation using the LMS adaptive filter--Dynamic behavior [J]. Acoustics, Speech and Signal Processing, IEEE Transactions on, 1981, 29 (3): 571-576.
[7] Wang Hongyu, Qiu Tianshuang. Adaptive noise cancellation and time delay estimation. Dalian: Dalian University of Technology Press, 1999.
[8] Qin Jingfan, Wei Gang. A variable step size LMS adaptive filtering algorithm based on sigmoid function [J]. Radio Engineering, 1996 (04): 44-47.
[9] Chen Lei. Simulation analysis of near-field source location algorithm under complex ocean noise environment [D]. Jilin University, 2015.
[10] Zhou Xingyue, Yang Kunde, A denoising representation framework for underwater acoustic signal recognition, Journal of the Acoustical Society of America, 2020, 147 (4): EL1-EL8.
[11] Chen Sijia. Research on adaptive filtering algorithm under Alpha stable distributed noise [D]. Hangzhou Dianzi University, 2020.
[12] Sun Yongmei, Qiu Tianshuang. A new method of HB weighted adaptive time delay estimation under fractional low-order α stable distribution noise [J]. Signal Processing, 2007 (03): 339-342.
[13] Zhao Ji. Research on adaptive filtering algorithm in Alpha stable distribution environment [D]. University of Electronic Science and Technology of China, 2020.
[14] Zhao Zhijin, Jin Mingming. The kernel minimum average P-norm algorithm under α stable distributed noise [J]. Application Research of Computers, 2017, 34 (11): 3308-3310+3315.
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Cite This Article
  • APA Style

    Keni Xu, Wenhong Liu. (2021). Generalized Weighted Adaptive Time Delay Estimation Algorithm Based on Minimum Average P Norm. Journal of Electrical and Electronic Engineering, 9(5), 161-169. https://doi.org/10.11648/j.jeee.20210905.13

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    ACS Style

    Keni Xu; Wenhong Liu. Generalized Weighted Adaptive Time Delay Estimation Algorithm Based on Minimum Average P Norm. J. Electr. Electron. Eng. 2021, 9(5), 161-169. doi: 10.11648/j.jeee.20210905.13

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    AMA Style

    Keni Xu, Wenhong Liu. Generalized Weighted Adaptive Time Delay Estimation Algorithm Based on Minimum Average P Norm. J Electr Electron Eng. 2021;9(5):161-169. doi: 10.11648/j.jeee.20210905.13

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  • @article{10.11648/j.jeee.20210905.13,
      author = {Keni Xu and Wenhong Liu},
      title = {Generalized Weighted Adaptive Time Delay Estimation Algorithm Based on Minimum Average P Norm},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {9},
      number = {5},
      pages = {161-169},
      doi = {10.11648/j.jeee.20210905.13},
      url = {https://doi.org/10.11648/j.jeee.20210905.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20210905.13},
      abstract = {The time delay estimation algorithm is one of the important factors of sound source localization. The generalized weighted adaptive time delay estimation algorithm is limited by the environmental conditions of signal and noise, and has great limitations in non-Gaussian environments. In order to make the algorithm suitable for non-Gaussian environments, and to retain the advantages of the algorithm in effectively suppressing harmonics, this paper combines the minimum average P norm (LMP) with the generalized weighting function, and proposes a method based on the minimum average P norm. The generalized weighted adaptive time delay estimation algorithm of the number can make the algorithm suitable for non-Gaussian environments, and for the shortcomings of slow iteration speed and large calculation amount for the minimum average P norm, the Sigmoid function is introduced to further improve the parameter selection in the algorithm. MATLAB simulation experiments show that the algorithm in this paper can effectively suppress the existence of harmonics in a non-Gaussian environment, and has strong convergence, high accuracy of time delay estimation, and fast iteration speed. It can be based on time delay estimation in a non-Gaussian environment. The positioning plays a certain role.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Generalized Weighted Adaptive Time Delay Estimation Algorithm Based on Minimum Average P Norm
    AU  - Keni Xu
    AU  - Wenhong Liu
    Y1  - 2021/10/16
    PY  - 2021
    N1  - https://doi.org/10.11648/j.jeee.20210905.13
    DO  - 10.11648/j.jeee.20210905.13
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 161
    EP  - 169
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20210905.13
    AB  - The time delay estimation algorithm is one of the important factors of sound source localization. The generalized weighted adaptive time delay estimation algorithm is limited by the environmental conditions of signal and noise, and has great limitations in non-Gaussian environments. In order to make the algorithm suitable for non-Gaussian environments, and to retain the advantages of the algorithm in effectively suppressing harmonics, this paper combines the minimum average P norm (LMP) with the generalized weighting function, and proposes a method based on the minimum average P norm. The generalized weighted adaptive time delay estimation algorithm of the number can make the algorithm suitable for non-Gaussian environments, and for the shortcomings of slow iteration speed and large calculation amount for the minimum average P norm, the Sigmoid function is introduced to further improve the parameter selection in the algorithm. MATLAB simulation experiments show that the algorithm in this paper can effectively suppress the existence of harmonics in a non-Gaussian environment, and has strong convergence, high accuracy of time delay estimation, and fast iteration speed. It can be based on time delay estimation in a non-Gaussian environment. The positioning plays a certain role.
    VL  - 9
    IS  - 5
    ER  - 

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Author Information
  • School of Electronic Information, Shanghai Dianji University, Shanghai, China

  • School of Electronic Information, Shanghai Dianji University, Shanghai, China

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