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Demand-Aware Opportunistic Spectrum Access: A Game-Theoretic Learning Approach Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Anomaly Detection Based on Kernel Principal Component and Principal Component Analysis

Authors : Wei Wang, Min Zhang, Dan Wang, Yu Jiang, Yuliang Li, Hongda Wu

Published in: Communications, Signal Processing, and Systems

Publisher: Springer Singapore

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Abstract

Nowadays, behind wall human detection based on UWB radar signal, which it had a strong anti-jamming performance, was an important problem. In this setting, principal component analysis (PCA) as an anomaly detection method was used, but PCA could only deal with linear data. Thus, we introduced the kernel principal component analysis (KPCA) for performing a nonlinear form of principal component analysis (PCA). We obtained the different state data based on UWB radar signal for the behind wall human detection. These data were used as training and testing data to calculate the squared prediction error (SPE) values that detect anomalies. The experimental results showed that the introduced approach of KPCA effectively captured the nonlinear relationship in the process data and showed superior process monitoring performance compared to linear PCA.

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Metadata
Title
Anomaly Detection Based on Kernel Principal Component and Principal Component Analysis
Authors
Wei Wang
Min Zhang
Dan Wang
Yu Jiang
Yuliang Li
Hongda Wu
Copyright Year
2019
Publisher
Springer Singapore
Book
Communications, Signal Processing, and Systems

Print ISBN: 978-981-10-6570-5

Electronic ISBN: 978-981-10-6571-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-10-6571-2_271