.png){kind=icon}
.png){kind=icon}
返回
Anomaly detection based on probability density function with Kullback-Leibler divergence
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2016.01.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Anomaly detection is a popular problem in many fields. We investigate an anomaly detection method based on probability density function (PDF) of different status. The constructed PDF only require few training data based on Kullback-Leibler Divergence method and small signal assumption. The measurement matrix was deduced according to principal component analysis (PCA). And the statistical detection indicator was set up under iid Gaussian Noise background. The performance of the proposed anomaly detection method was tested with through wall human detection experiments. The results showed that the proposed method could detection human being for brick wall and gypsum wall, but had unremarkable results for concrete wall. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Anomaly detection
Kullback-Leibler divergence
Principal component analysis
Through wall human detection
UWB radar
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。