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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.


AIC values for different AR models, compared for five vehicle trips.
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sensors-15-15540-f005: AIC values for different AR models, compared for five vehicle trips.

Mentions: For a set of data and a set of autoregressive models with different values of p, the best model is identified as the model that has the minimum AIC value. Figure 5 shows the AIC values for different autoregressive models with different orders, calculated for five different vehicles whose location information were extracted from the TAPASCologne vehicular mobility trace [24]. We can observe that the lowest AIC value for the majority of the vehicles is achieved when the autoregressive model order p is equal to 4, and that the model achieves an optimal balance between prediction accuracy and model complexity when. However, we will show through simulation experiments that the simplest order, at, is sufficient for prediction. The gain in prediction precision that is achieved by an autoregression model of order 4 is not large enough to justify the complexity cost incurred.


Improving Localization Accuracy: Successive Measurements Error Modeling.

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

AIC values for different AR models, compared for five vehicle trips.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15540-f005: AIC values for different AR models, compared for five vehicle trips.
Mentions: For a set of data and a set of autoregressive models with different values of p, the best model is identified as the model that has the minimum AIC value. Figure 5 shows the AIC values for different autoregressive models with different orders, calculated for five different vehicles whose location information were extracted from the TAPASCologne vehicular mobility trace [24]. We can observe that the lowest AIC value for the majority of the vehicles is achieved when the autoregressive model order p is equal to 4, and that the model achieves an optimal balance between prediction accuracy and model complexity when. However, we will show through simulation experiments that the simplest order, at, is sufficient for prediction. The gain in prediction precision that is achieved by an autoregression model of order 4 is not large enough to justify the complexity cost incurred.

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.