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Robust hyperbolic tangent Geman-McClure adaptive filter based on NKP decomposition and its performance analysis

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Abstract

For the identification of long impulse response systems in impulsive noise environments, existing algorithms have disadvantages such as slow convergence speed, large steady-state error, and poor tracking performance. In this brief, we propose the nearest Kronecker product decomposition based robust hyperbolic tangent Geman-McClure adaptive filter (NKP-HTGM) and analyze its performance. This algorithm uses the Geman-McClure function under hyperbolic tangent framework to remove the characteristic of the abnormal amplitude in the dataset, significantly improving the robustness against impulsive noise. Moreover, a novel variable step-size method (VSS) is introduced to further enhance the performance of NKP-HTGM (VSS-NKP-HTGM). Finally, the simulation results validate the effectiveness of the NKP-HTGM algorithm in system identification and the correctness of the theoretical analysis.

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The data that support the findings of this study are available on request from the corresponding author. The data are notpublicly available due to privacy or ethical restrictions.

References

  1. Diniz, P.S., et al.: Adaptive Filtering, vol. 4. Springer, New York (1997)

    Book  Google Scholar 

  2. Sayed, A.H.: Fundamentals of Adaptive Filtering. John Wiley & Sons, Boston, MA (2003)

    Google Scholar 

  3. Rupp, M., Schwarz, S.: A tensor lms algorithm. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3347–3351 (2015). IEEE

  4. Paleologu, C., Benesty, J., Ciochină, S.: Linear system identification based on a kronecker product decomposition. IEEE/ACM Trans. Audio Speech Lang. Process. 26(10), 1793–1808 (2018)

    Article  Google Scholar 

  5. Bhattacharjee, S.S., George, N.: Nearest kronecker product decomposition based linear-in-the-parameters nonlinear filters. Speech, and Language Processing, IEEE/ACM Transactions on Audio (2021)

    Book  Google Scholar 

  6. Elisei-Iliescu, C., Paleologu, C., Benesty, J., Stanciu, C., Anghel, C., Ciochină, S.: Recursive least-squares algorithms for the identification of low-rank systems. IEEE/ACM Trans. Audio Speech Lang. Process. 27(5), 903–918 (2019)

    Article  Google Scholar 

  7. Bhattacharjee, S.S., George, N.V.: Nearest kronecker product decomposition based normalized least mean square algorithm. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 476–480 (2020). IEEE

  8. Hameed, M.G.A., Tahaei, M.S., Mosleh, A., Nia, V.P.: Convolutional neural network compression through generalized kronecker product decomposition. arXiv preprint arXiv:2109.14710 (2021)

  9. Elisei-Iliescu, C., Paleologu, C., Benesty, J., Stanciu, C., Anghel, C., Ciochină, S.: A multichannel recursive least-squares algorithm based on a kronecker product decomposition. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 14–18 (2020). IEEE

  10. Bhattacharjee, S.S., George, N.V.: Fast and efficient acoustic feedback cancellation based on low rank approximation. Signal Process. 182, 107984 (2021)

    Article  Google Scholar 

  11. Dogariu, L.-M., Paleologu, C., Benesty, J., Ciochină, S.: An efficient kalman filter for the identification of low-rank systems. Signal Process. 166, 107239 (2020)

    Article  Google Scholar 

  12. Wodecki, J., Michalak, A., Zimroz, R.: Local damage detection based on vibration data analysis in the presence of gaussian and heavy-tailed impulsive noise. Measurement 169, 108400 (2021)

    Article  Google Scholar 

  13. Chen, J., Zhao, M., Wang, X., Richard, C., Rahardja, S.: Integration of physics- based and data-driven models for hyperspectral image unmixing: a summary of current methods. IEEE Signal Process Mag. 40(2), 61–74 (2023)

  14. Bershad, N.J., Bermudez, J.C.: A switched variable step size nlms adaptive filter. Digital Signal Process. 101, 102730 (2020)

    Article  Google Scholar 

  15. Wang, W., Dogancay, K.: Widely linear adaptive filtering based on clifford geo- metric algebra: a unified framework [hypercomplex signal and image processing]. IEEE Signal Process. Mag. 41(2), 86–101 (2024)

  16. Wang, S., Wang, W., Xiong, K., Iu, H.H., Chi, K.T.: Logarithmic hyperbolic cosine adaptive filter and its performance analysis. IEEE Trans. Syst. Man Cybern.: Syst. (2019)

  17. Chen, B., Príncipe, J.C.: Maximum correntropy estimation is a smoothed map estimation. IEEE Signal Process. Lett. 19(8), 491–494 (2012)

    Article  Google Scholar 

  18. Chen, B., Xing, L., Liang, J., Zheng, N., Principe, J.C.: Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion. IEEE Signal Process. Lett. 21(7), 880–884 (2014)

    Article  Google Scholar 

  19. Kumar, K., Pandey, R., Bora, S.S., George, N.V.: A robust family of algorithms for adaptive filtering based on the arctangent framework. IEEE Trans. Circuits Syst II: Express Br. 69(3), 1967–1971 (2022)

  20. Kumar, K., Pandey, R., Bhattacharjee, S.S., George, N.V.: Exponential hyperbolic cosine robust adaptive filters for audio signal processing. IEEE Signal Process. Lett. 28, 1410–1414 (2021)

    Article  Google Scholar 

  21. Chen, B., Xing, L., Zhao, H., Zheng, N., Prı, J.C., et al.: Generalized correntropy for robust adaptive filtering. IEEE Trans. Signal Process. 64(13), 3376–3387 (2016)

    Article  MathSciNet  Google Scholar 

  22. Bhattacharjee, S.S., Kumar, K., George, N.V.: Nearest kronecker product decomposition based generalized maximum correntropy and generalized hyperbolic secant robust adaptive filters. IEEE Signal Process. Lett. 27, 1525–1529 (2020)

    Article  Google Scholar 

  23. Liu, Q., He, Y.: Quaternion hyperbolic tangent geman-mcclure for adaptive filtering. IEEE Trans. Circuits Syst. II: Express Br., 1–1 (2024)

  24. Patel, V., Bhattacharjee, S.S., George, N.V.: Convergence analysis of adaptive exponential functional link network. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 882–891 (2020)

    Article  MathSciNet  Google Scholar 

  25. Kumar, K., Karthik, M., George, N.V.: A robust active noise control system based on an exponential hyperbolic cosine norm. Signal Process. 221, 109469 (2024)

    Article  Google Scholar 

  26. Yu, Y., Zhao, H., He, Z., Chen, B.: A robust band-dependent variable step size nsaf algorithm against impulsive noises. Signal Process. 119, 203–208 (2016)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Talent Introduction Program of Jiangsu University of Technology (No. KYY24010).

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Authors and Affiliations

  1. School of Electrical Engineering and Information Engineering, Jiangsu University of Technology, Changzhou, China

    Qianqian Liu

  2. School of Electronic Science and Engineering, Nanjing University, Nanjing, China

    Liulu He

  3. Shandong Lusoft Digital Technology Co., Jinan, China

    Shuguang Ning

Authors
  1. Qianqian Liu
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  2. Liulu He
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  3. Shuguang Ning
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Contributions

QL: Conceptualization, Methodology, Software, Writing-Original Draft, Writing-Review and Editing. LH: Methodology, Supervision, Writing-Review and Editing. SN: Writing-Software, Review and Editing.

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Correspondence to Liulu He.

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Liu, Q., He, L. & Ning, S. Robust hyperbolic tangent Geman-McClure adaptive filter based on NKP decomposition and its performance analysis. SIViP 18, 7755–7762 (2024). https://doi.org/10.1007/s11760-024-03425-5

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  • DOI: https://doi.org/10.1007/s11760-024-03425-5

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