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Position error correction for DGPS based localization using LSM and Kalman filter

It is generally important to get a precise position information for autonomous unmanned vehicle(AUV) in order to run safely. The GPS for getting the position has been using to navigate a vehicle(or AUV). But it is difficult to precisely control the AUV due to large measuring error of the GPS. Therefore, this paper proposes a method to more precisely localize AUV using three low-cost differential global positioning systems (DGPS). The distance errors between each DGPS are minimized as using the least square method (LSM) and the Kalman filter to eliminate a Gaussian white noise. The selected DGPS is cheaper and easier to set up than the RTK-GPS. It is also more precise than the general GPS. The proposed method can correct the relatively position error according to stationary distance of the AUV. For evaluating the algorithm by simulation, the DGPS signal with the Gaussian white noise to any points is generated by the AR model. The corrected position signal can be used to localize and control the AUV on the road.