AN EXTENDED KALMAN FILTER ALGORITHM FOR INTEGRATING GPS AND LOW COST DEAD RECKONING SYSTEM DATA FOR VEHICLE PERFORMANCE AND EMISSIONS MONITORING

In this paper, the authors describe the features of an extended Kalman filter algorithm for supporting the navigational function of a real- time vehicle performance and emissions monitoring system that is currently under development. The Kalman filter is for processing Global Positioning System (GPS) data enhanced with dead reckoning (DR) in an integrated mode, in order to provide continuous positioning in built-up areas. The dynamic model and filter algorithms are described as well as the findings based on computer simulations and a limited field trial carried out in the Greater London area. Results show that using the extended Kalman filter algorithm allows the integrated system using GPS and low cost DR devices to meet the required navigation performance of the device.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03734633
• Supplemental Notes:
  • Publication Date: May 2003
• Corporate Authors:

  Imperial College of Science, Technology, and Medicine

  London,   England 
• Authors:
  • Zhao, L
  • Ochieng, W Y
  • Quddus, M A
  • Noland, R B
• Publication Date: 2003

Language

• English

Media Info

• Pagination: p. 257-275
• Serial:
  • The Journal of navigation. Vol. 56, no. 2
  • Publisher: Imperial College of Science, Technology, and Medicine
  • ISSN: 0373-4633

Subject/Index Terms

• TRT Terms: Dead reckoning; Global Positioning System; Kalman filtering

Filing Info

• Accession Number: 00961272
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH
• Created Date: Aug 4 2003 12:00AM