Improving positioning accuracy for VANET in real city environments

Tsai, Ming-Fong; Wang, Po-Ching; Shieh, Ce-Kuen; Hwang, Weng-Sing; Chilamkurti, Naveen; Rho, Seungmin; Lee, Yang Sun

doi:10.1007/s11227-014-1215-9

Cited by 30 publications

(18 citation statements)

References 14 publications

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“…The objective of cooperative positioning is to utilize the network localization resources by allowing neighboring nodes to work together to cooperatively improve the accuracy of their location via the periodic exchange of location information. The focus of research in this area is centered on three main themes: integrating ego measurements with measurements sent by neighbors [ 2 , 6 , 7 ], assessment of neighbors’ location measurements by analyzing the communication message characteristics [ 4 , 8 , 9 , 10 ], and integrating ego or neighbor measurements with map information [ 3 ]. Cooperative schemes were proposed to fuse data pertaining to multiple sensors within the vehicle together with data received from other neighbor vehicles in order to obtain relative position estimates [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

“…Cooperative schemes were proposed to fuse data pertaining to multiple sensors within the vehicle together with data received from other neighbor vehicles in order to obtain relative position estimates [ 6 ]. Cameras were used as sensors that provide relative distance measurements [ 2 ], and those measurements were fused with the location information exchanged among vehicles. Some schemes incorporate only GPS information to compute inter-vehicle distances for cooperative localization [ 7 ].…”

Section: Related Workmentioning

confidence: 99%

“…Global Navigation Satellite Systems such as the Global Positioning System (GPS) have been widely used to provide localization for mobile nodes. However, it has been established that these systems may not always provide dependable position information, due mostly to signal deterioration in urban environments [ 1 , 2 ]; contexts in which emerging vehicular applications increasingly require accurate localization.…”

Section: Introductionmentioning

confidence: 99%

“…Research in localization accuracy is focused on the incorporation of additional data sources in the localization process in order to enhance the accuracy of a vehicle’s GPS measurements. Example localization techniques are the fusion of data from other sensors [ 2 ], the use of anchor landmarks and maps [ 3 ], and cooperative positioning, the verification and correction of location data among neighbor nodes [ 4 , 5 ]. However, deploying these methods may incur additional costs and increase the complexity of the localization scheme.…”

Section: Introductionmentioning

confidence: 99%

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Improving Localization Accuracy: Successive Measurements Error Modeling

Ali

Abu-Elkheir

2015

Sensors

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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 p-order Gauss–Markov model to predict the future position of a vehicle from its past p 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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Localization Accuracy: Successive Measurements Error Modeling

Ali

Abu-Elkheir

2015

Sensors

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“…And target to be recovered is usually represented by a matrix. However, it often has the problem of missing data or the noise pollution (Tsai et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Compromising Location Privacies for Vehicles Cloud Computing

Lin¹

2018

IJWGS

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In this paper, we propose an enhanced vehicular crowdsourcing localisation and tracking (EVCLT) scheme for mounting a trajectory tracking attack in vehicular cloud computing environment. In our scheme, crowdsourcing technique is applied to sample the location information of certain users. Then matrix completion technique is used to generate our predictions of the users' trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulation results reveal that the proposed approach is able to accurately track the trajectories of certain users.

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RIALS: RSU/INS‐aided localization system for GPS‐challenged road segments

Zarza

Yousefi

Benslimane

2015

Wireless Communications

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This paper introduces a new vehicle localization approach for global positioning system-challenged road segments (e.g., tunnels), which takes advantage of roadside units (RSUs) and in-vehicle inertial navigation system (INS). In the proposed approach, namely RSU/INS-aided localization system (RIALS), vehicles only need one RSU in their transmission range for an accurate positioning. The beacons received from the RSU along with the information provided by the INS system are used for establishing and maintaining particular locus circles. After linearization, the system of locus circle equations is solved using the linear least square estimation technique, and the estimated vehicle's position is obtained. In the presence of speed variations and existing ranging and INS estimation errors, the proposed RIALS adaptively sets the sufficient number of required locus circles, aiming at keeping the localization error below a given threshold. We study the effects of ranging and INS estimation errors on localization accuracy of RIALS from analytical and simulation perspectives. Results of extensive simulations show that the localization error is more sensitive to the ranging error than the INS error. Moreover, the network traffic overhead of the proposed method is considerably lower than other competitive localization approaches.

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Improving positioning accuracy for VANET in real city environments

Cited by 30 publications

References 14 publications

Improving Localization Accuracy: Successive Measurements Error Modeling

Improving Localization Accuracy: Successive Measurements Error Modeling

Compromising Location Privacies for Vehicles Cloud Computing

RIALS: RSU/INS‐aided localization system for GPS‐challenged road segments

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