Open AccessVehicle positioning system with multi‐hypothesis map matching and robust feedback
Author(s) -
Zhang Kai,
Liu Shaojun,
Dong Yuhan,
Wang Daoshun,
Zhang Yi,
Miao Lixin
Publication year - 2017
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0072
Subject(s) - map matching , computer science , matching (statistics) , artificial intelligence , computer vision , global positioning system , mathematics , statistics , telecommunications
A new vehicle positioning system is proposed using unscented Kalman filter for the data fusion of global positioning system and inertial navigation system, and a multi‐hypothesis algorithm for map matching. The study presents a method to evaluate whether the results of the multi‐hypothesis map matching algorithm can be used for feedback, and a strategy to increase the positioning accuracy based on this feedback. As the number of hypothesis nodes in the multi‐hypothesis map matching algorithm grows exponentially with time, which costs lots of computation time and memory, several methods are proposed to reduce the number of hypotheses nodes by improving the generation method of hypothesis nodes, pruning the branches of multi‐hypothesis tree, eliminating and merging the redundant nodes. Field test results indicate that the system can achieve much higher accuracy with the feedback from map matching, and can greatly save the computation time and memory.