NDLI: Vehicle positioning system with multi-hypothesis map matching and robust feedback

Content Provider	IET Digital Library
Author	Zhang, Kai Liu, Shaojun Dong, Yuhan Wang, Daoshun Zhang, Yi Miao, Lixin
Abstract	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.
Starting Page	649
Ending Page	658
Page Count	10
ISSN	1751956X
Volume Number	11
e-ISSN	17519578
Issue Number	Issue 10, Dec (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/11/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0072
Journal	IET Intelligent Transport Systems
Publisher Date	2017-06-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Data Fusion Filtering Method in Signal Processing Global Positioning System Hypothesis Node Generation Method Inertial Navigation System Kalman Filter Multihypothesis Algorithm Multihypothesis Map Matching Multihypothesis Tree Branches Nonlinear Filter Positioning Accuracy Radio Navigation And Direction Finding Redundant Node Elimination Redundant Node Merging Road Vehicle Robust Feedback Sensor Fusion Traffic Engineering Computing Unscented Kalman Filter Vehicle Positioning System
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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