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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.


Longitudinal positioning error produced by the standard Kalman filter and the Autoregression-Kalman filter for vehicle.
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sensors-15-15540-f010: Longitudinal positioning error produced by the standard Kalman filter and the Autoregression-Kalman filter for vehicle.

Mentions: The process and measurement noise in Kalman filter-based localization algorithms are usually assumed to be zero-mean Gaussian white noise. Our proposed error model can compute the noise variance in location information. Therefore, we incorporate it into the prediction step of the Kalman filter and study how this will improve the localization algorithm. The-integrated Kalman filter is then applied to the two time series location information of vehiclesand, and the results shown in Figure 10, Figure 11, Figure 12 and Figure 13 compare the localization error for the three error models;,, and. We note that the computational complexity of the Kalman filter and the autoregression-integrated Kalman filter is dominated by the matrix multiplication, and therefore grows as, whereis the number of location measurements. We also note that the computational complexity of the AR-integrated Kalman filter implementation and the standard Kalman filter implementation are identical. This is because the integration of the AR model into the Kalman filter involves the replacement of the transition and error matrices and the extension of prediction step to accommodate computations whenand.


Improving Localization Accuracy: Successive Measurements Error Modeling.

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

Longitudinal positioning error produced by the standard Kalman filter and the Autoregression-Kalman filter for vehicle.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15540-f010: Longitudinal positioning error produced by the standard Kalman filter and the Autoregression-Kalman filter for vehicle.
Mentions: The process and measurement noise in Kalman filter-based localization algorithms are usually assumed to be zero-mean Gaussian white noise. Our proposed error model can compute the noise variance in location information. Therefore, we incorporate it into the prediction step of the Kalman filter and study how this will improve the localization algorithm. The-integrated Kalman filter is then applied to the two time series location information of vehiclesand, and the results shown in Figure 10, Figure 11, Figure 12 and Figure 13 compare the localization error for the three error models;,, and. We note that the computational complexity of the Kalman filter and the autoregression-integrated Kalman filter is dominated by the matrix multiplication, and therefore grows as, whereis the number of location measurements. We also note that the computational complexity of the AR-integrated Kalman filter implementation and the standard Kalman filter implementation are identical. This is because the integration of the AR model into the Kalman filter involves the replacement of the transition and error matrices and the extension of prediction step to accommodate computations whenand.

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.