The development of vehicle position estimation algorithms based on the use of AMR sensors

Saber Taghvaeeyan, Rajesh Rajamani

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


This paper focuses on the use of anisotropic magnetoresistive (AMR) sensors for imminent crash detection in cars. The AMR sensors are used to measure the magnetic field from another vehicle in close proximity to estimate relative position and velocity from the measurement. An analytical formulation for the relationship between magnetic field and vehicle position is developed. The challenges in the use of the AMR sensors include their nonlinear behavior, limited range, and magnetic signature levels that vary with each type of car. An adaptive filter based on the iterated extended Kalman filter (IEKF) is developed to automatically tune filter parameters for each encountered car and to reliably estimate relative car position. The utilization of an additional sonar sensor during the initial detection of the encountered vehicle is shown to highly speed up the parameter convergence of the filter. Experimental results are presented from a number of tests with various vehicles to show that the proposed sensor system is viable.

Original languageEnglish (US)
Article number6280668
Pages (from-to)1845-1854
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
StatePublished - 2012

Bibliographical note

Funding Information:
Manuscript received October 26, 2011; revised February 21, 2012; accepted June 9, 2012. Date of publication August 23, 2012; date of current version November 27, 2012. This work was supported in part by funding from the Intelligent Transportation Systems Institute, University of Minnesota. The Associate Editor for this paper was H. Dia.


  • Crash detection
  • crash sensors
  • magnetic sensors
  • vehicle position sensors


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