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

This poster describes a Python computational tool for exploring the use of the extended Kalman filter (EKF) for Global Positioning (GPS) position estimation using pseudorange measurements. The development was motivated by the need for an example generator in a training class on Kalman filtering, with emphasis on GPS. 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 SGP4 and the two-line element (TLE) data available from celestrak.

Mark  WickertSpeaker: Mark Wickert, University of Colorado
 Speaker: Chiranth Siddappa