Improving Localization Accuracy: Successive Measurements Error Modeling

Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. The mathematical models used in current vehicular localization schemes focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, the authors first investigate the existence of correlation between successive positioning measurements, and then incorporate this correlation into the modeling positioning error. The authors use the Yule Walker equations to determine the degree of correlation between a vehicle’s future position and its past positions, and then propose a -order Gauss–Markov model to predict the future position of a vehicle from its past  positions. The authors investigate the existence of correlation for two datasets representing the mobility traces of two vehicles over a period of time. The authors prove the existence of correlation between successive measurements in the two datasets, and show that the time correlation between measurements can have a value up to four minutes. Through simulations, the authors validate the robustness of our model and show that it is possible to use the first-order Gauss–Markov model, which has the least complexity, and still maintain an accurate estimation of a vehicle’s future location over time using only its current position. The authors model can assist in providing better modeling of positioning errors and can be used as a prediction tool to improve the performance of classical localization algorithms such as the Kalman filter.

• Record URL:
  • https://doi.org/10.3390/s150715540
• Record URL:
  • http://www.mdpi.com/1424-8220/15/7/15540
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • © 2015 Najah Abu Ali and Mervat Abu-Elkheir.
• Authors:
  • Ali, Najah Abu
  • Abu-Elkheir, Mervat
• Publication Date: 2015

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: pp 15540-15561
• Serial:
  • Sensors
  • Volume: 15
  • Issue Number: 7
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Errors; Markov processes; Mathematical models; Mobility; Positioning; Simulation; Vehicle safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01603202
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2016 4:22PM