Increasing DGPS Navigation Accuracy using Kalman Filter Tuned by Genetic Algorithm

Global Positioning System (GPS) is being used in aviation, nautical navigation and the orientation ashore. Further, it is used in land surveying and other applications where the determination of the exact position is required. Although GPS is known as a precise positioning system, there are several error sources which are categorized into three main groups including errors related to satellites, propagation and GPS receivers. Regarding wide applications of GPS systems and the importance of its accuracy, these exiting errors could be averted by Differential GPS (DGPS) method. In this paper, a Kalman Filter (KF)-based algorithm which is adapted with Genetic Algorithm (GA) is proposed to reduce errors in GPS receivers. The model's validity is verified by experimental data from an actual data collection. Using the practical implementations the experimental results are provided to illustrate the effectiveness of the model. The experimental results suggest that it is possible to reduce position RMS errors in single-frequency GPS receivers to less than 1 meter. Accordingly, effective error value improves to 0.4873 meter utilizing KF adapted with GA as compared to traditional KF.