An Integration Algorithm for Urban Kinematic Mapping: A Kalman Filter Solution Using a Modified Form of the GPS Triple Difference and Dead Reckoning Observables.

摘要

To improve kinematic mapping performance duringperiods of partial satellite visibility, this paper presents thealgorithms developed to integrate GPS carrier phase,vehicle odometer and gyroscope rotation measurements todetermine the position and orientation of a movingvehicle.Based around the standard Kalman filter algorithms, ameasurement model has been derived which has atranslational component and a rotational, or orientationcomponent. The translational component continuouslytracks the change in vehicle position over time usingmodified GPS triple difference carrier phasemeasurements and measurements of distance from thevehicle odometer to update predictions from a constantacceleration dynamic model. The rotational componenttracks the orientation of the vehicle coordinate frameusing measurements of orientation change provided by