Improving Localization Accuracy: Successive Measurements Error Modeling

Sensors

Najah Abu Ali¹  Mervat Abu-Elkheir² 

[1] College of Information Technology, United Arab Emirates University, Al-Ain 15551, Abu Dhabi;Faculty of Computer and Information Sciences, Mansoura University, Mansoura 35516, Egypt; E-Mail:

关键词: localization;    Gauss–Markov model;    location prediction;   

DOI  :  10.3390/s150715540

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.