The Development of Vehicle Position Estimation Algorithms Based on the Use of AMR Sensors

Abstract

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.