A robust least mean M-estimate adaptive filtering algorithm based on geometric algebra for system identification

Shaohui Lv; Haiquan Zhao

doi:10.1117/12.2589392

20 January 2021 A robust least mean M-estimate adaptive filtering algorithm based on geometric algebra for system identification

Shaohui Lv, Haiquan Zhao

Author Affiliations +

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171915 (2021) https://doi.org/10.1117/12.2589392
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

In this paper, a novel robust algorithm called geometric algebra least mean M-estimate (GA-LMM) is proposed, which is the extension of the conventional LMM algorithm in GA space. To further improve the convergence performance, variable step-size GA-LMM (VSS-GA-LMM) algorithm is also proposed, which effectively balances the trade-off between convergence rate and steady-state misalignment. Finally, a multidimensional system identification problem is considered to verify the performance of the proposed GA-LMM and VSS-GA-LMM algorithms. Simulation results show that the proposed algorithms are superior to other GA-based algorithms in terms of convergence rate and steady-state misalignment in impulsive noise environments.

Citation Download Citation

Shaohui Lv and Haiquan Zhao "A robust least mean M-estimate adaptive filtering algorithm based on geometric algebra for system identification", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171915 (20 January 2021); https://doi.org/10.1117/12.2589392

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