Pedestrian Tracking and Navigation Using Neural Networks and Fuzzy Logic

The main goal of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate GPS, micro-electro-mechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the initial performance analyses of the personal navigator prototype
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, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. To facilitate this functionality, the adaptive knowledge system, based on the Artificial Neural Networks (ANN) and Fuzzy Logic, is trained during the GPS signal reception and used to maintain navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL) are estimated during the system calibration period, then the predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3-5 m CEP (circular error probable) 50%.