SISO Radar-Based Human Movement Direction Determination Using Micro-Doppler Signatures

Abstract

Human movement direction determination (HMDD) is a significant task in human detection and recognition applications, but it remains a challenge when utilizing single-input and single-output (SISO) radar because angle information cannot be accessed without multiple receiving antennas. Moreover, adopting multiple-output radar systems for this task would limit their applicability in a broader range of scenarios, as these systems require a larger placement area and a more extensive calibration procedure than SISO radar. Tackling this problem, this paper presents an effective method for the SISO-radar-based human movement direction determination task. Our method combines the joint time-frequency analysis (JTFA) technique with a proposed bio-inspired feature extraction process, thereby producing an accurate perception of moving direction based on the analysis of micro-Doppler signatures. The radar simulation and measurements are separately conducted and used to establish the corresponding dataset, so the superior performance of our method could be verified on the HMDD task. Furthermore, this article delves into why existing criteria for evaluating an HMDD model’s performance are insufficient in multi-direction situations, followed by an introduction of “small error concentration" and “omnidirectional error uniformity", as well as their evaluation protocols, to describe and measure the bias of an HMDD model’s results on multi-direction determination problems. By comparing with existing both traditional and deep-learning methods, we confirm our method’s superior in the HMDD task, and we believe that our research will aid in the advancement of human detection and recognition applications using SISO radar.