TDoA and RSS based Extended Kalman Filter for Indoor Person Localization

Pedestrian localization systems require the knowledge of a users position for manifold applications in indoor and outdoor environments. For this purpose severel methods can be used, such as a Global Navigation Satellite System (GNSS) or Inertial Navigation Systems (INS). Since GNSS are not available in indoor environments or street canyons, in this paper a 802.15.4a network is used to estimate the pedestrian's position. The used network platform provides the Time Difference of Arrival (TDoA) as well as the Received Signal Strength (RSS). To fuse both measurement types a novel method is implemented which is based on the Extended Kalman Filter (EKF). Due to the low accuracy of RSS it is ignored if the TDoA system performs well. But if the TDoA measurements are affected by multipath propagation or other effects the RSS values are used to identify those situations and to correct the estimated position of the user. To evaluate the algorithm experimental results in two different environments are presented.