PERFORMANCE ASSESSMENT OF A MULTU-SENSOR PERSONAL NAVIGATOR SUPPORTED BY AN ADAPTIVE KNOWLEDGE BASED SYSTEM

摘要

The prototype of a personal navigator 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 provides a review of the navigation techniques suitable for personal navigation and follows with design, implementation and performance assessment of the system prototype, with a special emphasis on the dead-reckoning (DR) navigation supported by the human locomotion model. An adaptive knowledge system (KBS) based on Artificial Neural Networks (ANN) and Fuzzy Logic (FL) has been implemented to support this functionality. The KBS is trained a priori using sensory data collected by various operators in various environments during the GPS signal reception, and is used to support navigation under GPS-denied conditions. The primary components of the human locomotion model are step frequency (SF) and step length (SL). SL is determined by a predictive model derived by the KBS during the system's calibration/training period. SL is correlated with several sensory and environmental data types, such as acceleration, acceleration variation, SF, terrain slope, operator's height, etc. That constitute the input parameters to the KBS system. The KBS-predicted SL, together with the heading information provided by the magnetometer and/or gyroscope, supports the DR navigation. The current target accuracy of the system is 3-5 m CEP (circular error probable, 50%). A summary of the performance analysis in the mixed indoor-outdoor environments, with the special emphasis on the DR performance is provided.