一种用于噪声数据序列异常检测的无模型Kullback-Leibler散度滤波器
A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series
AI总结：
本文提出一种无模型KLD滤波器，用于从接近传感器噪声数据序列中提取异常位置和相对大小，并在工业标准设置中验证其性能优于同类工业算法。
摘要：
我们提出一种Kullback-Leibler散度（KLD）滤波器，用于从一大类接近传感器生成的数据序列中提取异常，并同时提取异常位置及其相对大小。该技术适用于工程实践中常用的设备，例如安装在移动机器人上、用于对危险或其他人类可能无法直接进入的环境进行无损检测的设备。由于噪声相对于信号内容占主导地位，这类传感器产生的原始数据可能难以分析。所提出的滤波器旨在检测传感器采集的数据序列与基线数据序列之间的信息内容差异。它既适用于基于模型的场景，也适用于无模型场景。KLD滤波器的性能在工业标准设置中得到了验证，并与一个同类工业采用的算法进行了基准对比。
英文摘要：
We propose a Kullback-Leibler Divergence (KLD) filter to extract anomalies within data series generated by a broad class of proximity sensors, along with the anomaly locations and their relative sizes. The technique applies to devices commonly used in engineering practice, such as those mounted on mobile robots for non-destructive inspection of hazardous or other environments that may not be directly accessible to humans. The raw data generated by this class of sensors can be challenging to analyze due to the prevalence of noise over the signal content. The proposed filter is built to detect the difference of information content between data series collected by the sensor and baseline data series. It is applicable in a model-based or model-free context. The performance of the KLD filter is validated in an industrial-norm setup and benchmarked against a peer industrially-adopted algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
