A Fuzzy Dead Reckoning Algorithm for a Personal Navigator

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Abstract
A concept design of a fuzzy Dead Reckoning (DR) algorithm for a personal navigator (PN) is introduced here. The PN system prototype includes a range of self-contained sensors such as GPS, accelerometer, gyroscope, magnetometer, digital barometer, and step sensors. In addition, a human locomotion model is considered as a navigation sensor, with the step length (SL) and step direction (SD) as primary parameters. The major focus of this paper is on DR navigation supported by human dynamics during GPS signal blockages. It is demonstrated that in the absence of GPS, the other sensors can sense the body locomotion in terms of its dynamics and geometry that represent an implicit function of SL and SD. A practical implementation of the DR system based on human dynamics is a fuzzy logic Knowledge-Based System (KBS). This paper discusses the design and implementation of the KBS, followed by its performance evaluation in the indoor environments.