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Improving Localization Accuracy: Successive Measurements Error Modeling.

Ali NA, Abu-Elkheir M - Sensors (Basel) (2015)

Bottom Line: Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks.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.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 Article: PubMed Central - PubMed

Affiliation: College of Information Technology, United Arab Emirates University, Al-Ain 15551, Abu Dhabi. najah@uaeu.ac.ae.

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 -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.

No MeSH data available.


OpenStreetMap route for verification dataset 3.
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sensors-15-15540-f018: OpenStreetMap route for verification dataset 3.

Mentions: In order to verify ourmodel, we tested the model against three realistic datasets. We chose three vehicular mobility traces from OpenStreetMap [30]. The first trace (), which is illustrated in Figure 16, represents a single vehicle trip along the route between Northeast Union Hill Road and Bravern 1, Bellevue, WA. The total trip time was 30 min and 45 s (totaling 1845 data points), and vehicle speed ranged from 0 km/h to 104.4 km/h. The second trace (), illustrated in Figure 17, represents a single vehicle trip between East Main Street and the Gelder Park, Derry, PA, USA. The total trip time was 5 min and 45 s (totaling 343 data points), and speed ranged from 0 km/h to 96.7 km/h. The third trace (), illustrated in Figure 18, depicts a vehicle’s trip around a new residential area in Stockholm, Sweden. The total vehicle trip time was 25 min and 56 s (totaling 1313 data points). There is no mention of the vehicle speeds for the third dataset. For the three datasets, location measurements were taken every second.


Improving Localization Accuracy: Successive Measurements Error Modeling.

Ali NA, Abu-Elkheir M - Sensors (Basel) (2015)

OpenStreetMap route for verification dataset 3.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541843&req=5

sensors-15-15540-f018: OpenStreetMap route for verification dataset 3.
Mentions: In order to verify ourmodel, we tested the model against three realistic datasets. We chose three vehicular mobility traces from OpenStreetMap [30]. The first trace (), which is illustrated in Figure 16, represents a single vehicle trip along the route between Northeast Union Hill Road and Bravern 1, Bellevue, WA. The total trip time was 30 min and 45 s (totaling 1845 data points), and vehicle speed ranged from 0 km/h to 104.4 km/h. The second trace (), illustrated in Figure 17, represents a single vehicle trip between East Main Street and the Gelder Park, Derry, PA, USA. The total trip time was 5 min and 45 s (totaling 343 data points), and speed ranged from 0 km/h to 96.7 km/h. The third trace (), illustrated in Figure 18, depicts a vehicle’s trip around a new residential area in Stockholm, Sweden. The total vehicle trip time was 25 min and 56 s (totaling 1313 data points). There is no mention of the vehicle speeds for the third dataset. For the three datasets, location measurements were taken every second.

Bottom Line: Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks.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.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 Article: PubMed Central - PubMed

Affiliation: College of Information Technology, United Arab Emirates University, Al-Ain 15551, Abu Dhabi. najah@uaeu.ac.ae.

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 -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.

No MeSH data available.