Random forest-based human pose detection system for through-the-wall radar

Abstract

Nowadays, the detection, identification and classification of targets behind buildings in the process of wall penetration sensing is one of the main solution directions for detection activities. In this paper, a random forest-based human pose detection system for through-wall radar is proposed, aiming at optimizing the traditional through-wall radar target detection and identification by machine learning methods. The actual data acquisition is performed by UWB-MIMO through-wall radar system to construct multidimensional data and identify the pose. The experimental results show that the random forest method has high recognition performance by identifying multiple poses and has a pose resolution that traditional target recognition does not have.