Unknown Input Observer-Based Filterings for Mobile Pedestrian Localization Using Wireless Sensor Networks

This paper proposes a pedestrian localization technique using a wireless sensor network. An unknown input observer (UIO)-based Kalman filter and UIO-based H_∞ filter are newly derived for the pedestrian localization. The purpose of the developed filters is to ensure a decoupling of unknown acceleration inputs generated by motions of the pedestrian from the state estimation and to minimize the external disturbance effects. Through comparative simulation and experimental tests, we evaluate the performance of the developed filters.