Position Measurement Under Uncertainty Using Magnetic Field Sensing

This paper presents a method for position measurement under uncertainty using magnetic sensing. The statistical transformation of magnetic field data in the presence of noise is first developed. An unscented Kalman filter is then formulated based on a stochastic dynamic model that would allow for position estimation from magnetic field sensing. Finally, applications of magnetic sensing-based positioning in robotics and vehicle guidance are provided to validate the algorithm. The methods presented in this paper extend filtering theory for extracting positioning information from magnetic fields.