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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 positioning error produced by Autoregression-Kalman filter for dataset 2.
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sensors-15-15540-f022: Latitudinal positioning error produced by Autoregression-Kalman filter for dataset 2.

Mentions: As with the previous experiment set, the model parameters and process noise were extracted from each of the datasets using the autoregression model of order 1. The Kalman filter was integrated with the model parameters and applied to the location measurements. Figure 19, Figure 20, Figure 21, Figure 22, Figure 23 and Figure 24 show, respectively, the longitudinal and latitudinal position measurement errors produced by the Kalman filter when the Gauss–Markov model is used for the prediction step when applied to the OpenStreetMap traces. The margin of error for all three datasets stays within a very small and negligible range (0.005 m), which indicates the high level of accuracy produced by the localization model. The larger variation in longitudinal error in comparison to the latitudinal error inis due to the larger variation rate in longitudinal measurements compared to a small overall change in the latitudinal measurements.


Improving Localization Accuracy: Successive Measurements Error Modeling.

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

Latitudinal positioning error produced by Autoregression-Kalman filter for dataset 2.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15540-f022: Latitudinal positioning error produced by Autoregression-Kalman filter for dataset 2.
Mentions: As with the previous experiment set, the model parameters and process noise were extracted from each of the datasets using the autoregression model of order 1. The Kalman filter was integrated with the model parameters and applied to the location measurements. Figure 19, Figure 20, Figure 21, Figure 22, Figure 23 and Figure 24 show, respectively, the longitudinal and latitudinal position measurement errors produced by the Kalman filter when the Gauss–Markov model is used for the prediction step when applied to the OpenStreetMap traces. The margin of error for all three datasets stays within a very small and negligible range (0.005 m), which indicates the high level of accuracy produced by the localization model. The larger variation in longitudinal error in comparison to the latitudinal error inis due to the larger variation rate in longitudinal measurements compared to a small overall change in the latitudinal measurements.

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.