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Wavelet Robust Filtering of Out-trajectory Data

机译:小波稳健滤波超轨迹数据

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摘要

Wavelet-based robust filtering of process data is proposed in order to reduce the influence of the outliers and noise in Out-trajectory data. We utilize the moving median filtering method to reject outliers in the original data and then combine wavelet de-noising method with empirical Wiener threshold to suppress noise. Simulation calculation and real engineering application has shown that the novel algorithm reliably preserves the information encapsulated in a process signal corrupted with noise and outliers. The methodology has been proved to be reliable and robust.
机译:提出了基于小波的鲁棒滤波过程数据,以减少异常值和横向数据中的噪声的影响。我们利用移动中值滤波方法来拒绝原始数据中的异常值,然后将小波去噪方法与经验维纳阈值相结合以抑制噪声。仿真计算和实际工程应用表明,新颖算法可靠地保留封装在噪声和异常值损坏的过程信号中的信息。该方法已被证明是可靠和强大的。

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