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.

As the core part of the new generation of information technology, the Internet of Things has accumulated a large number of real-time data streams of various types and structures. The data stream is generated at an extremely fast speed, and its content and distribution characteristics are all in high-speed dynamic changes, which must be processed in real time. Therefore, the feature learning algorithm is required to support incremental updates and learn the characteristics of high-speed dynamic change data in real time. Most of the current machine learning models for processing big data belong to the static learning model. The batch learning method makes it impossible to analyze data streams in real time, and the learning ability of dynamic data streams is poor. Therefore, this paper proposes an incremental high-order deep learning model to extend the data from the vector space to the tensor space and update the parameters and structure of the network model in the high-order tensor space. In the process of parameter updating, the first-order approximation concept is introduced to avoid incrementing parameters by the iterative method and to improve the parameter update efficiency, so that the updated model can quickly learn the characteristics of dynamically changing big data and satisfy the real-time requirements of big data feature learning while maintaining the original knowledge of the neural network model as much as possible. To evaluate the performance of the proposed model, experiments were performed on real image data sets-MNIST, and the model was evaluated for stability, plasticity, and run time. The experimental results show that the model not only has the ability to incrementally learn the characteristics of new data online but also retains the ability to learn the original data features, improve the model update efficiency, and maximize the online analysis and real-time processing of dynamic data streams.