Multi-sensor personal navigator supported by adaptive knowledge based system: Performance assessment

Abstract

The prototype of a personal navigator, which integrates Global Positioning System (GPS), tactical grade inertial measurement unit (IMU), digital barometer, magnetometer, and human pedometry to support navigation and tracking of military and rescue ground personnel has been developed at The Ohio State University Satellite Positioning and Inertial Navigation (SPIN) Laboratory. This paper discusses the design, implementation and performance assessment of the prototype, with a special emphasis on dead-reckoning (DR) navigation supported by a human locomotion model. The primary components of the human locomotion model are step frequency (SF), extracted from GPS-timed impact micro-switches placed on the shoe soles of the operator, step length (SL), and step direction (SD), both determined by predictive models derived by the adaptive knowledge based system (KBS). SL KBS is based on Artificial Neural Networks (ANN) and Fuzzy Logic (FL), and is trained a priori using sensory data collected by various operators in various environments during GPS signal reception. An additional KBS module, in the form of a Kalman Filter (KF), is used to improve the heading information (SD) available from the magnetometer and gyroscope under GPS-denied conditions, as well as to integrate the DR parameters to reconstruct the trajectory based on SL and SD. The current target accuracy of the system is 3-5 m CEP (circular error probable, 50%). This paper provides a performance analysis in the indoor and outdoor environments for two different operators. The systempsilas navigation limitation in DR mode is tested in terms of time and trajectory length to determine the upper limit of indoor operation before the need for re-calibration.