Foot-Mounted Inertial Navigation - Implementation and Fusion Concept into a Bayesian Filtering Framework

A reliable underground navigation system for task forces in emergency scenarios increases safety and effectiveness. Since smoke, explosions or disturbed power supply complicate the operations belowground, the correct visualization of the task forces’ positions has become a key element in obtaining a successful outcome. A foot-mounted inertial navigation system provides an autonomous way to navigate in Global Navigation Satellite Systems (GNSS)-denied environments. The additional use of maps and absolute positioning methods (Wi-Fi Round Trip Time (RTT), Ultra-Wideband (UWB), ...) represent one way to build a robust navigation system for emergency operations underground. This study proposes a sensor fusion concept to combine a foot-mounted inertial navigation system (INS) with distance measurements and map information. However, the main emphasis in this paper is on the implementation of a foot-mounted INS. Therefore, a Zero-Velocity-Aided Error-State (Extended) Kalman Filter is utilized. The filter is based on the mechanization equation that expresses the position in the earth-fixed frame, the velocity in the local-level frame and the attitude in the body frame. The performance analysis of the foot-mounted INS as a single system showed that a stable position solution over several hundred meters could be achieved.