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Robust hyperbolic tangent Geman-McClure adaptive filter based on NKP decomposition and its performance analysis.
- Source :
- Signal, Image & Video Processing; Nov2024, Vol. 18 Issue 11, p7755-7762, 8p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18631703
- Volume :
- 18
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- Signal, Image & Video Processing
- Publication Type :
- Academic Journal
- Accession number :
- 179636342
- Full Text :
- https://doi.org/10.1007/s11760-024-03425-5