The  PNLMS algorithm with unbiasedness criterion in sparse system identification

Linzhi Song; Min Zhu; YouXiang Chen; Li Wang; Xv Nuo; ZhiGang Hao

doi:10.1117/12.2629159

7 March 2022 The PNLMS algorithm with unbiasedness criterion in sparse system identification

Linzhi Song, Min Zhu, YouXiang Chen, Li Wang, Xv Nuo, ZhiGang Hao

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 1216736 (2022) https://doi.org/10.1117/12.2629159
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

The proportionate normalized least mean square (PNLMS) algorithm is used in sparse system identification for its simplicity and adaptively step-size adjusting scheme. However, the PNLMS has the noisy input and the non-Gaussian output noise problem. A bias Compensated PNLMF algorithm (called BCPNLMF) for identifying sparse system has been proposed to solve aforementioned issues. The BCPNLMF algorithm which takes advantage of the bias compensated and the proportionate scheme, can achieve better steady-state accuracy and faster convergence speed besides identify the system parameters in noisy input and output with non-Gaussian character environments. Simulation results carried out in sparse system identification confirm the remarkable performance of the BCPNLMF, compared with other well-known algorithms.

Citation Download Citation

Linzhi Song, Min Zhu, YouXiang Chen, Li Wang, Xv Nuo, and ZhiGang Hao "The PNLMS algorithm with unbiasedness criterion in sparse system identification", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 1216736 (7 March 2022); https://doi.org/10.1117/12.2629159

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