Indoor multi-person tracking via ultra-wideband radars

Abstract

Tracking multiple objects in indoor environments has various applications such as patient monitoring and inventory tracking. In this thesis, the use of Gaussian mixture probability hypothesis density (GM-PHD) filters is investigated for multiple person tracking via ultra-wideband (UWB) radar sensors in an indoor environment. An experimental setup consisting of a network of UWB radar sensors and a high-speed computer is designed and a new detection algorithm is proposed. The results of this experimental proof-of-concept study show that it is possible to accurately track multiple targets using a UWB radar sensor network in indoor environments based on the proposed approach.