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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.


Latitudinal position error for vehicle v1 using autoregression model with p = 1, 2, 4.
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sensors-15-15540-f007: Latitudinal position error for vehicle v1 using autoregression model with p = 1, 2, 4.

Mentions: The model parametersthat were produced by the Yule Walker equations for the two mobility traces for vehiclesandare listed in Table 1. For the first set of experiments (standalone prediction), the results in Figure 6 and Figure 7 show the errors in the longitudinal (x) and latitudinal (y) position estimates made by the Gauss–Markov model forfor vehicle. The figures show that higher model orders correspond to reduced estimation error of the future location coordinates of the vehicle;andcan predict the vehicle’s location with an average error of 11 m for xand 16 m for y, with inconsiderable improvement made by the model withcompared to the model with. However, when, the average error is 55 m for both x and y. This is further corroborated by the results produced when applying the same Gauss–Markov model on the location measurements of vehicle, which has been designated for validation, as shown in Figure 8 and Figure 9. We can conclude thatis a reasonable compromise between complexity and accuracy and therefore is sufficient for depolyment of the error model as a standalone prediction tool. The tradeoff performance provided by the model whenwill make the model’s prediction robust even when used in fast-fading channel scenarios, which may exhibit lower correlation windows, since the correlation window in this case is small enough to allow for correlation to be manifested, while still producing accurate prediction. This makes the model applicable in different scenarios with potentially varying correlation windows.


Improving Localization Accuracy: Successive Measurements Error Modeling.

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

Latitudinal position error for vehicle v1 using autoregression model with p = 1, 2, 4.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15540-f007: Latitudinal position error for vehicle v1 using autoregression model with p = 1, 2, 4.
Mentions: The model parametersthat were produced by the Yule Walker equations for the two mobility traces for vehiclesandare listed in Table 1. For the first set of experiments (standalone prediction), the results in Figure 6 and Figure 7 show the errors in the longitudinal (x) and latitudinal (y) position estimates made by the Gauss–Markov model forfor vehicle. The figures show that higher model orders correspond to reduced estimation error of the future location coordinates of the vehicle;andcan predict the vehicle’s location with an average error of 11 m for xand 16 m for y, with inconsiderable improvement made by the model withcompared to the model with. However, when, the average error is 55 m for both x and y. This is further corroborated by the results produced when applying the same Gauss–Markov model on the location measurements of vehicle, which has been designated for validation, as shown in Figure 8 and Figure 9. We can conclude thatis a reasonable compromise between complexity and accuracy and therefore is sufficient for depolyment of the error model as a standalone prediction tool. The tradeoff performance provided by the model whenwill make the model’s prediction robust even when used in fast-fading channel scenarios, which may exhibit lower correlation windows, since the correlation window in this case is small enough to allow for correlation to be manifested, while still producing accurate prediction. This makes the model applicable in different scenarios with potentially varying correlation windows.

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.