Next Article in Journal
A Sensitive and Selective Label-Free Electrochemical DNA Biosensor for the Detection of Specific Dengue Virus Serotype 3 Sequences
Next Article in Special Issue
Investigating Driver Fatigue versus Alertness Using the Granger Causality Network
Previous Article in Journal
UAV-Based Photogrammetry and Integrated Technologies for Architectural Applications—Methodological Strategies for the After-Quake Survey of Vertical Structures in Mantua (Italy)
Previous Article in Special Issue
Radar Sensing for Intelligent Vehicles in Urban Environments
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(7), 15540-15561;

Improving Localization Accuracy: Successive Measurements Error Modeling

College of Information Technology, United Arab Emirates University, Al-Ain 15551, Abu Dhabi
Faculty of Computer and Information Sciences, Mansoura University, Mansoura 35516, Egypt
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 4 March 2015 / Revised: 11 June 2015 / Accepted: 19 June 2015 / Published: 1 July 2015
(This article belongs to the Special Issue Sensors in New Road Vehicles)
Full-Text   |   PDF [2695 KB, uploaded 1 July 2015]   |  


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 -order Gauss–Markov model to predict the future position of a vehicle from its past 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. View Full-Text
Keywords: localization; Gauss–Markov model; location prediction localization; Gauss–Markov model; location prediction

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ali, N.A.; Abu-Elkheir, M. Improving Localization Accuracy: Successive Measurements Error Modeling. Sensors 2015, 15, 15540-15561.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top