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access icon free Vehicle positioning system with multi-hypothesis map matching and robust feedback

  • Author(s): Kai Zhang 1, 2 1, 2 ; Shaojun Liu 1 2 ; Yuhan Dong 1, 2 1, 2 ; Daoshun Wang 3 3 ; Yi Zhang 1, 2, 4 1, 2, 4 ; Lixin Miao 1, 2 1, 2
    • View affiliations
    • Affiliations: 1: Tsinghua-Berkeley Shenzhen Institute, Shenzhen, People's Republic of China ;
      2: Graduate School at Shenzhen, Tsinghua University, Shenzhen, People's Republic of China ;
      3: Department of Computer Science and Technology, Tsinghua University, Beijing, People's Republic of China ;
      4: Department of Automation, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, People's Republic of China
  • Source: Volume 11, Issue 10, December 2017, p. 649 – 658
    DOI:  10.1049/iet-its.2017.0072 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 28/02/2017, Accepted 23/06/2017, Revised 02/06/2017, Published 28/06/2017

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.

Inspec keywords: nonlinear filters; road vehicles; sensor fusion; inertial navigation; traffic engineering computing; Global Positioning System; Kalman filters

Other keywords: global positioning system; positioning accuracy; vehicle positioning system; data fusion; inertial navigation system; redundant node merging; multihypothesis algorithm; robust feedback; redundant node elimination; multihypothesis map matching; unscented Kalman filter; multihypothesis tree branches; hypothesis nodes generation method

Subjects: Radionavigation and direction finding; Sensor fusion; Traffic engineering computing; Filtering methods in signal processing

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