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Exploring the Extended Kalman Filter for GPS Positioning Using Simulated User and Satellite Track Data

Mark Wickert
University of Colorado Colorado Springs

Chiranth Siddappa
University of Colorado Colorado Springs


This paper describes a Python computational tool for exploring the use of the extended Kalman filter (EKF) for position estimation using the Global Positioning System (GPS) pseudorange measurements. The development was motivated by the need for an example generator in a training class on Kalman filtering, with emphasis on GPS. In operation of the simulation framework both user and satellite trajectories are played through the simulation. The User trajectory is input in local east-north-up (ENU) coordinates and satellites tracks, specified by the C/A code PRN number, are propagated using the Python package SGP4 using two-line element (TLE) data available from Celestrak.


Global positioning system, Kalman filter, Extended Kalman filter,



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