Anomaly Detection Based on Kernel Principal Component and Principal Component Analysis

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.