Abrupt change detection is critical to monitor the occurrence of abnormal events from sensor data for situational awareness of complex systems. However, various disturbances and noises applied to the data observations may pose significant challenges to the robustness of many abrupt change detection methods. Recent researches have shown that bilateral filter can acquire outstanding performance on removing noises from images while preserving edge information. In this letter, we propose two improved edge-preserving memory-based cumulative sum (MB-CUSUM) methods that are able to make the abrupt change detection method more robust against noises. Our experimental studies show that the proposed methods can achieve superior performance over state-of-the-art methods to detect abrupt changes, which demonstrates the effectiveness and feasibility of their practical use.

中文翻译：

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用于突变检测的基于内核的边缘保留方法

突然变化检测对于从传感器数据监控异常事件的发生以实现复杂系统的态势感知至关重要。然而，应用于数据观测的各种干扰和噪声可能对许多突变检测方法的鲁棒性构成重大挑战。最近的研究表明，双边滤波器可以在去除图像噪声的同时保留边缘信息方面获得出色的性能。在这封信中，我们提出了两种改进的基于边缘保留内存的累积和 (MB-CUSUM) 方法，它们能够使突变检测方法对噪声更加鲁棒。我们的实验研究表明，与最先进的方法相比，所提出的方法可以在检测突然变化方面取得更好的性能，