Adaptive recursive algorithm with logarithmic transformation for nonlinear system identification in alpha-stable noise

作者：Zhao Haiquan^*; Lu Lu; He Zhengyou; Chen Badong

来源：Digital Signal Processing, 2015, 46: 120-132.

DOI：10.1016/j.dsp.2015.08.004

• alpha-stable noise
• Nonlinear system identification
• Logarithmic transformation
• Volterra filter

Although the least mean pth power (LMP) and normalized LMP (NLMP) algorithms of adaptive Volterra filters outperform the conventional least mean square (LMS) algorithm in the presence of alpha-stable noise, they still exhibit slow convergence and high steady-state kernel error in nonlinear system identification. To overcome these limitations, an enhanced recursive least mean pth power algorithm with logarithmic transformation (RLogLMP) is proposed in this paper. The proposed algorithm is adjusted to minimize the new cost function with the p-norm logarithmic transformation of the error signal. The logarithmic transformation, which can diminish the significance of outliers under alpha-stable noise environment, increases the robustness of the proposed algorithm and reduces the steady-state kernel error. Moreover, the proposed method improves the convergence rate by the enhanced recursive scheme. Finally, simulation results demonstrate that the proposed algorithm is superior to the LMP, NLMP, normalized least mean absolute deviation (NL1VIAD), recursive least squares (RLS) and nonlinear iteratively reweighted least squares (NIRLS) algorithms in terms of convergence rate and steady-state kernel error.

• 出版日期2015-11
• 单位西安交通大学; 西南交通大学