A Data Fusion Approach for Speed Estimation and Location Calibration of a Metro Train in Underground Environment Based on Low-Cost Sensors in Smartphones

Using sensors in a smartphone has been proven to be an inexpensive and feasible approach to measure the car body vibration of a train. The upcoming problems are to estimate the running speed and build the relationship between the time-axis and the location-axis. This problem becomes extremely challenging when GPS system is not available. This paper provides a low-cost and convenient data-fusion approach to estimate the speed and location of a metro train in underground environment simply using the 3-axis accelerometers in smartphones. Multiple smartphones are distributed at different position on a train. A data-based model is established to fuse all the measured accelerations. Firstly, the longitudinal acceleration provides detailed but biased information of speed and location by integral. Secondly, the lateral and vertical accelerations are used to provide absolute reference for speed estimation. The local time delay and similarity between the waveforms of different smartphones are estimated. The waveform similarity is used as the weight to balance the uncertainty of each estimated time-delay-based speed. Thirdly, an updating process is proposed to fuse the integral-based speed and time-delay-based speed. At last, a case study is carried out by applying the method on Chengdu Metro. Results show that the data-fusion approach can greatly enhance the performance of speed estimation. The total estimated error of interval distance between two adjacent stations are below 20 meters comparing to the ground truth values, even better for longer interval, indicating the method proposed in this paper is able to control the integral error of acceleration.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AP000 Public Transportation Group. Alternate title: A Data Fusion Approach for Speed Estimation and Location Calibration of a Metro Train Based on Low-Cost Sensors in Smartphones
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Wang, Yuan
    • Cong, Jianli
    • Tang, Huiyue
    • Liu, Xiang
    • Gao, Tianci
    • Wang, Ping
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01697552
  • Record Type: Publication
  • Report/Paper Numbers: 19-02737
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:31AM