Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Modern monostatic radar-based human activity recognition (HAR) systems perform very well as long as the direction of human activities is either toward or away from the radar. The monostatic single-input-single-output (SISO) and monostatic multiple-input-multiple-output (MIMO) radar systems cannot detect motion of an object that moves perpendicularly to the radar's boresight axis. Due to this physical layer limitation, today's radar-based HAR systems fail to classify multidirectional human activities. In this article, we resolve this typical but critical physical layer problem of contemporary HAR systems. We propose a HAR system underlying a distributed MIMO radar configuration, where multiple antennas of a millimeter wave (mm-wave) MIMO radar system (Ancortek SDR-KIT 2400T2R4) are distributed in an indoor environment. In our proposed HAR system, we have two independent and identical monostatic radar subsystems that irradiate and capture the multidirectional human movement from two perspectives, which allows to compute two distinct time-variant (TV) radial velocity distributions. A feature extraction network extracts numerous features from the measured TV radial velocity distributions, which are then fused by a multiclass classifier to detect five types of human activities. The proposed multiperspective MIMO-radar-based HAR system achieves a classification accuracy of 98.52%, which surpasses the accuracy of SISO radar-based HAR system by more than 9%. Our approach resolves the physical layer limitations of modern HAR systems that are based on either monostatic SISO or monostatic MIMO radar systems.