Abstract
A Kalman filter was developed to improve the DGPS position estimates for a parallel tracking application.Applying Kalman filtering to raw DGPS measurement data effectively removes the DGPS noise and reduces therootmeansquared (RMS) positioning error. In our study, the maximum crosstracking error (XTE) was reduced from 9.83 mto 2.76 m by Kalman filtering. The Kalman filter also reduced the rootmeansquared XTE from 0.58 m to 0.56 m. In thedirection of travel, the Kalman filter had much smaller positioning error than the mean filter; the RMS positioning errors inthe direction of travel were 1.35 m for the mean filter and 0.26 m for the Kalman filter. The GPS bias error was the majorsource of the crosstracking error. Further study is recommended to estimate and reduce the GPS bias error for paralleltracking applications.
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