Multirate sensor fusion for GPS using Kalman filtering, fuzzy methods, and map matching
With the advent of the Global Position System (GPS), we now have the ability to determine absolute position anywhere on the globe. Although GPS system work well in open environments with no overhead obstructions, they re subject to large unavoidable errors when the reception from some of the satellites is blocked. This occurs frequently in urban environments, such as downtown New York City. GPS systems require at least four satellites visible to maintain a good position 'fix', and tall buildings and tunnels often block several, if not all, of the satellites. Additionally, due to selective availability, where small amounts of error are intentionally introduced, GPS errors can typically range up to 100 ft or more. This paper proposes several methods for improving the position estimation capabilities of a system by incorporating other sensor and data technologies, including Kalman filtered inertial navigation system, rule- based and fuzzy-based senors fusion techniques, and a unique map-matching algorithm.
Detectors; Error-correcting codes (Information theory); Global Positioning System; Kalman filtering
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Multirate sensor fusion for GPS using Kalman filtering, fuzzy methods, and map matching.
Proceedings of SPIE, 3525
Society of Photo-optical Instrumentation Engineers (SPIE).