Human indoor localization based on ceiling mounted PIR sensor nodes

This paper presents a human indoor localization system using ceiling mounted pyroelectric infrared (PIR) sensors. The field of views (FOVs) of the PIR sensors is modulated by two degrees of freedom (DOF) of spatial segmentation. The localization algorithm is proposed to fuse the data stream generated from different sensor nodes within the wireless network. The Kalman Filter and Kalman Smoother are utilized to refine the estimation of the human position. We conduct experiments in a real office environment, and the average root-mean-square error (RMSE) of single human target tracking at different speed is about 0.6 meter. The promising results confirm the efficacy of our system.