Through-Wall Human Activity Classification Using Complex-Valued Convolutional Neural Network
Xiang Wang, Pengyun Chen, Hangchen Xie, Guolong Cui
Abstract
Deep learning has attracted intensive attention in human activity classification based on the radar. Whereas, most methods use the images to classify the human activities, ignoring the phase information of the radar data. In this paper, the complex-valued convolutional neural network (Complex-valued CNN) is utilized to classify the human activity behind the wall. We developed several Complex-valued CNN models, which have the same structures as several classical convolutional neural network(CNN) models and use both the amplitude and phase information of the range profiles. Experiments on the real data validate the performance of the Complex-valued CNN models.
Topics & Concepts
Convolutional neural networkComputer scienceArtificial intelligenceDeep learningRadarArtificial neural networkPattern recognition (psychology)Machine learningTelecommunicationsAdvanced SAR Imaging TechniquesRadar Systems and Signal ProcessingGait Recognition and Analysis