A family of robust adaptive filtering algorithms based on sigmoid cost

作者：Huang, Fuyi; Zhang, Jiashu^*; Zhang, Sheng

来源：Signal Processing, 2018, 149: 179-192.

DOI：10.1016/j.sigpro.2018.03.013

• Adaptive filter
• Least mean fourth
• Least mean square
• Maximum correntropy criterion
• Robust adaptive filtering

In this paper, a new framework of cost function for designing robust adaptive filtering algorithms is developed. This new cost framework, called sigmoid cost function, results from imbedding the conventional cost function into the sigmoid framework. Utilizing this proposed sigmoid cost framework, several important members (e.g., the sigmoid least mean square, sigmoid least absolute difference, sigmoid least mean fourth, sigmoid least mean logarithmic square, sigmoid least logarithmic absolute difference algorithms) of this family of robust adaptive filtering algorithms are proposed. In the proposed sigmoid cost frame-work with a steepness parameter alpha, a greater (smaller) value of alpha results in a slower (faster) convergence. Thus, an adaptive alpha is proposed which is based on an exponential function with respect to the L-1-norm of the system error. In addition, the mean-square deviation analysis of the proposed algorithms is also carried out and their accuracies are verified via system identification experiment. Simulations in system identification and acoustic echo-cancellation scenarios have demonstrated that the proposed algorithms outperform the corresponding order's generalized maximum correntropy criterion, normalized least mean square using step-size scaler, sign algorithm and least logarithmic absolute difference algorithms.

• 出版日期2018-8
• 单位西南交通大学