Exponential Myriad Smoothing Algorithm for Robust Signal Processing in α-Stable Noise Environments

The sequential sample myriad has been proposed recently to estimate an unknown location parameter in real time by updating the current estimate when a new input sample is available. However, the algorithm is only capable of estimating an unknown constant (i.e., a time-invariant location parameter). In this paper, we propose a sequential myriad smoothing approach for tracking a time-varying location parameter corrupted by impulsive symmetric αα -stable noise. By incorporating exponential weighting factor to the sequential algorithm, the new algorithm weighs the recent samples more heavily to provide effective tracking capability. Simulation results show that the proposed method outperforms the classical exponential smoothing and is as good as the running myriad smoother