Enhancement of GPS Position Accuracy Using Machine Vision and Deep Learning Techniques

The accuracy of GPS position estimation in urban cities is an issue which need to be resolved using machine vision and deep learning techniques. The accuracy of GPS in horizontal direction is better than in the vertical direction. Although for most of the navigation applications in intelligent transportation systems, horizontal positioning accuracy is vital, but vertical position accuracy gives idea about road slanting conditions. Several statistical methods like median filtering, homomorphic filtering and k-means clustering, etc., can be used to improve upon the position accuracy of GPS signals. Such methods are useful for offline applications where a lot many GPS measurements are taken at a single point and afterwards filtering is applied to batch of measurement. In this study, the GPS positioning errors which are caused by sensor noise, ionospheric effects, occlusions by building facades, etc., have been considered for online improvement in position estimation using computer vision and deep learning methods by empirically choosing hyper-parameters.