Abstract
Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. The mathematical models used in current vehicular localization schemes focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, we first investigate the existence of correlation between successive positioning measurements, and then incorporate this correlation into the modeling positioning error. We use the Yule Walker equations to determine the degree of correlation between a vehicle’s future position and its past positions, and then propose a p-order Gauss–Markov model to predict the future position of a vehicle from its past p positions. 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 four 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 future location over time using only its current position. 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.
Highlights
Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks
The contribution of this paper is three-fold; we establish the time correlation of successive location measurement errors, we define a -order Gauss–Markov model that can be used for the prediction of future location while taking positioning error into account, and we use this model to enhance the accuracy of the prediction step in existing Kalman filter implementations with no need for data fusion
We applied the Kalman filter that was adjusted by the Gauss–Markov model parameters to a real-life dataset in order to assess the accuracy of the enhanced localization scheme and rule out overfitting
Summary
Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. Since vehicles exhibit a predictable and constrained behavior in terms of mobility, we anticipate that the future position of a vehicle will be correlated with its previous positions Verifying this hypothesis via our proposed model can help improve the localization accuracy without incurring additional costs in terms of complexity. The contribution of this paper is three-fold; we establish the time correlation of successive location measurement errors, we define a -order Gauss–Markov model that can be used for the prediction of future location while taking positioning error into account, and we use this model to enhance the accuracy of the prediction step in existing Kalman filter implementations with no need for data fusion.
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