Real Time Pseudo-Range Correction Predicting by a Hybrid GASVM model in order to Improve RTDGPS Accuracy

Differential base station sometimes is not capable of sending correction information for minutes, due to radio interference or loss of signals. to overcome the degradation caused by the loss of differential global positioning system (dgps) pseudo-range correction (prc), predictions of prc is possible. in this paper, the support vector machine (svm) and genetic algorithms (gas) will be incorporated for predicting dgps prc information. the genetic algorithm is employed to feature subset selection. online training for real-time prediction of the prc enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy in real time dgps. given a set of data received from low cost gps module, the gasvm can predict the prc precisely when the prc signal is lost for a short period of time. this method which is introduced for the first time for prediction of prc is compared to other recently published methods. the experiments show that the total rms prediction error of gasvm is less than 0.06m for on step and 0.16m for 10 second ahead cases