Robust testing for stationarity in the presence of outliers
Robust testing for stationarity in the presence of outliers
复制标题
存在异常值时稳健性测试
DOI：
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复制
发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Zoubir
中科院分区：
文献类型：
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作者：
J. Dagdagan;Michael Muma;A. Zoubir
在各种信号处理应用中都需要测试随机过程的平稳性。在处理现实问题时，异常值和脉冲（重尾）噪声的存在会导致经典的平稳性测试失效。本文提出了一套基于球度统计检验（SST）的频域鲁棒平稳性检验。研究了不同可能的方法，并将其与现有的稳健性和非稳健性平稳性检验进行了比较，以确定受试者工作特征（ROC）。除了广泛的模拟之外，还研究了一个故障窗口调节器电机的实际数据示例，该故障窗口调节器电机的主导频率显示出调制特性，导致非平稳信号。无论是模拟数据还是真实数据，所提出的方法都明显优于现有方法。
Testing the stationarity of stochastic processes is required in a variety of signal processing applications. When dealing with real-world problems, the presence of outliers and impulsive (heavy-tailed) noise causes classical stationarity tests to break down. In this work, a set of robust stationarity tests that are based on a sphericity statistic test (SST) in the frequency domain is proposed. Different possible approaches are investigated and compared to existing robust and non-robust stationarity tests in terms of the receiver operating characteristic (ROC). In addition to extensive simulations, a real-world data example of a malfunctioning window regulator motor, for which the dominant frequencies show a modulating character that results in a non-stationary signal, is investigated. Both for simulated and real-world data, the proposed methods significantly outperform existing approaches.