An improved affine projection tanh algorithm for robust adaptive filtering

Pucha Song; Haiquan Zhao; Li Luo

doi:10.1117/12.3023131

28 March 2024 An improved affine projection tanh algorithm for robust adaptive filtering

Pucha Song, Haiquan Zhao, Li Luo

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130910G (2024) https://doi.org/10.1117/12.3023131
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

Recently, an affine projection tanh (APT) algorithm has been designed based on optimization criteria with hyperbolic tangent function constraints. However, there is a significant steady-state error in the APT algorithm. To address this drawback, this article presents an improved APT (IAPT) algorithm based on the optimization framework with hyperbolic tangent function square constraints. It is shown that the proposed IAPT algorithm displays strong robustness, higher convergence speed, and smaller estimation error compared to affine projection (AP) algorithm, AP sign algorithm (APSA) and APT algorithms under the impulsive noise disturbance in the system identification application scenario.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pucha Song, Haiquan Zhao, and Li Luo "An improved affine projection tanh algorithm for robust adaptive filtering", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130910G (28 March 2024); https://doi.org/10.1117/12.3023131

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available