Sensor Fusion with Extended Kalman Filter Applied to Indoor Tracking: Comparing RSSI and RTT

The following work aims at presenting the proposal of using Extended Kalman Filter (EKF) sensor fusion applied to the indoor tracking and navigation issue. Two sensor fusion approaches were proposed. The first one called IMU+RSSI fuses the acceleration data of an Inertial Measurement Unit (IMU) with Received Signal Strength Indicator (RSSI) data provided by wireless access points. The second one called IMU+RTT fuses IMU data and distance measurements provided by access points using Round Trip Time (RTT). The resultant trajectories given by both fusion methods are compared to a reference path through simulations results based on real sensor characteristics. As expected, the IMU+RTT fusion showed better results, even considering multirate sensors. The proposed methods are promising to implement tracking and navigation systems in indoor environments.