In this paper, a new adaptive robustified filter algorithm of recursive weighted least squares with combined scale and variable forgetting factors for time-varying parameters estimation in non-stationary and impulsive noise environments has been proposed. To reduce the effect of impulsive noise, whether this situation is stationary or not, the proposed adaptive robustified approach extends the concept of approximate maximum likelihood robust estimation, the so-called M robust estimation, to the estimation of both filter parameters and noise variance simultaneously. The application of variable forgetting factor, calculated adaptively with respect to the robustified prediction error criterion, provides the estimation of time-varying filter parameters under a stochastic environment with possible impulsive noise. The feasibility of the proposed approach is analysed in a system identification scenario using finite impulse response (FIR) filter applications.

中文翻译：

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使用结合了比例尺和遗忘因子的递归加权最小二乘进行稳健的自适应滤波。

针对非平稳和脉冲噪声环境下的时变参数估计问题，提出了一种新的自适应鲁棒加权最小二乘自适应鲁棒滤波算法，该算法结合了比例尺和遗忘因子，用于时变参数估计。为了减小脉冲噪声的影响，无论这种情况是否稳定，所提出的自适应鲁棒方法将近似最大似然鲁棒估计（即所谓的M鲁棒估计）的概念扩展到同时估计滤波器参数和噪声方差。相对于鲁棒的预测误差准则自适应计算的变量遗忘因子的应用，提供了在随机环境下可能带有脉冲噪声的时变滤波器参数的估计。