Server Based Real Time GPS-IMU Integration Aided by Fuzzy Logic Based Map Matching Algorithm for Car Navigation
The stand-alone Global Positioning System (GPS) or an Integrated GPS and Dead Reckoning Systems (such as Inertial Navigation System or Odometer and magnetometer) have been widely used for vehicle navigation. An essential process in such an application is to map match the position obtained from GPS (or/and other sensors) on a digital road network map.
GPS positioning is relatively accurate in open sky conditions, but its position is not accurate in dense urban canyon conditions where GPS is affected by signal blockage and multipath. High sensitivity GPS (HS GPS) receivers, can increase the availability, but are affected by multipath and cross correlation due to weak signal tracking. Inertial navigation system can be used to bridge GPS gaps, However, position and velocity results in such conditions are typically biased, therefore, fuzzy logic based map matching, is mostly used because it can take noisy, imprecise input, to yield crisp (i.e. numerically accurate) output. Fuzzy logic can be applied effectively to map match the output from a High sensitivity GPS receiver or integrated GPS and INS in urban canyons because of its inherent tolerance to imprecise inputs.
In this thesis stand-alone GPS positioning and integrated GPS and Inertial Measurement Unit (IMU) positioning aided by fuzzy logic based map matching for Stockholm urban and suburban areas are performed. A comparison is carried out between, Map matching for stand-alone GPS and integrated GPS and IMU. Stand-alone GPS aided map matching algorithms identifies 96.4% of correct links for rural area, 92.6% for urban area (car test) and 93.4% for bus test in urban area. Integrated GPS and IMU aided map matching algorithms identifies 97.3% of correct links for rural area, 94.4% for urban area (car test) and 94.4% for bus test in urban area. Integrated GPS and Inertial Measurement Unit produces better vehicle azimuth than stand-alone GPS, especially at low speed. Furthermore, there are five more fuzzy rules based on gyro rate in integrated GPS and IMU map matching algorithm. Therefore, it shows better map matching results. GPS blackout happens rarely in Stockholm, because there are not many tall buildings in this city. Therefore, the integrated GPS and IMU aided by map matching shows only small improvement over stand-alone GPS aided by map matching.
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