Passive Multiuser Gait Identification Through Micro-Doppler Calibration Using mmWave Radar

User identification, especially multi-user identification, plays an important role in IoT-enabled smart spaces. The early wearable or vision-based solutions either cause discomfort or suffer from privacy leakage, and the radio frequency (RF)-based methods are appreciated in recent years. Compared with other RF technologies, the millimeter wave (mmWave) has the merit of high spatial resolution and has been widely employed in wireless sensing. In this paper, we present a multi-user gait identification system based on micro-Doppler calibration (MCGait) using a commodity mmWave radar. With the raw signals as the input, MCGait first extracts the point clouds with a pipeline of signal preprocessing and separates them using a spatial cluster algorithm for multi-target tracking. Then, MCGait conducts a velocity calibration with a virtual radar-based method, and calibrates temporal gait micro-Doppler features for each user, so as to eliminate the negative effect of gait direction dynamics. Finally, the calibrated features are fed into a neural network to identify all the users. We implement MCGait on a commodity 77GHz mmWave radar and conduct extensive experiments to validate its performance. The experimental results show that the proposed MCGait can achieve up to 98.50% single-user recognition accuracy, and over 95.45% identification accuracy for up to four users.