Presents the results obtained in our research about application of modern nonlinear filtering techniques to GPS based position estimation. The stand-alone GPS based position estimation problem using GPS pseudo-range and Doppler shifts measurements are described. A model for position and velocity estimation are developed. The model is nonlinear and has variable measurement number for coping with an arbitrary number of satellites. Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. In this work the use of an alternative filter: the unscented Kalman filter (UKF) is proposed. The first experimental results that comprise the comparison of estimation results obtained with a simple model using different filters are then presented. Future research directions are also discussed.
Investigation on nonlinear filtering algorithms for GPS
Intelligent Vehicle Symposium, 2002. IEEE ; 1 ; 64-70 vol.1
2002-01-01
398705 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
NONLINEAR FILTERING ALGORITHMS FOR GPS USING PSEUDORANGE AND DOPPLER SHIFT MEASUREMENTS
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