Sparse least mean fourth adaptive algorithm for censored regression

Bing Chen; Haiquan Zhao; Yingying Zhu

doi:10.1117/12.2588927

20 January 2021 Sparse least mean fourth adaptive algorithm for censored regression

Bing Chen, Haiquan Zhao, Yingying Zhu

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 117190S (2021) https://doi.org/10.1117/12.2588927
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

In the linear systems, the conventional least mean fourth (LMF) algorithm has faster convergence and lower steady-state error than LMS algorithm, However, in many applications, the censored observations occur frequently. In this paper, a least mean fourth (LMF) algorithm with censored regression is proposed for adaptive filtering. When the identified system possesses a certain extent of sparsity, the least mean fourth algorithm for Censored Regression (CRLMF) algorithm may encounter performance degradation. Therefore, a reweighted zero-attracting LMF algorithm based on the censored regression model (RZA-CRLMF) is proposed further. Simulations are carried out in system identification and echo cancellation scenarios. The results verify the effectiveness of the proposed CRLMF and RZA-CRLMF algorithms. Moreover, in sparse system, the RZA-CRLMF algorithm improves further the filter performance in terms of the convergence speed and the mean squared deviation for the presence of sub-Gaussian noise.

Citation Download Citation

Bing Chen, Haiquan Zhao, and Yingying Zhu "Sparse least mean fourth adaptive algorithm for censored regression", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 117190S (20 January 2021); https://doi.org/10.1117/12.2588927

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