Extended Kalman filter for a low cost TDoA/IMU pedestrian localization system

Abstract

Pedestrian localization systems require the knowledge of a user's position for manifold applications in indoor and outdoor environments. For this purpose several methods can be used, such as a Global Navigation Satellite System (GNSS) or an Inertial Navigation Systems (INS). Since GNSS are not available in indoor environments or street canyons a Time Difference of Arrival (TDoA) system and a low cost Inertial Measurement Unit (IMU), which consists of an accelerometer and a gyroscope, is used to estimate the position of a pedestrian. The localization device is mountable to different positions of the body, like the hip or the pocket of a shirt. The measurements of the IMU are prefiltered to get steps, the step length and fast changings in the user's orientation. To fuse the different measurement types an Extended Kalman Filter (EKF) is applied. To evaluate the algorithm experimental results are presented.