Regression forecasting model to improve localization accuracy

Abstract

Location information with a high level of accuracy is a crucial component in many of the emerging services provided to users by wireless and mobile networks. The proposed mathematical models for localization focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, we first investigate the correlation between successive positioning measurements, and then take this correlation into consideration when modeling positioning error. We propose a p-order Gauss-Markov model to predict the future position of a mobile node from its current mobility statistics, and use the Yule Walker equations to determine the degree of correlation between a node's future position and its past positions. Using vehicular networks as a case study, we investigate the existence of correlation for two datasets representing the mobility traces of two vehicles over a period of time. We prove the existence of correlation between successive measurements in the two datasets, and show that the time correlation between measurements can have a value up to 4 minutes. Through simulations, we validate the robustness of our model and show that it is possible to use the first-order Gauss-Markov model, which has the least complexity, and still maintain an accurate estimation of a vehicle's location over time. Our model can assist in providing better modeling of positioning errors and can be used as a prediction tool to improve the performance of classical localization algorithms such as the Kalman filter.