Abstract
In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consider reducing traffic accidents by exchanging position information between pedestrians and vehicles by vehicle-to-pedestrian communication, we require accurate position information for pedestrians and vehicles. The GPS (global positioning system) is the most widely used method for acquiring position information. However, in urban areas, the GPS signal is affected by the surrounding buildings, which increases the positioning error. In this study, a method to improve the positioning accuracy of pedestrians using the signal strengths from vehicles and beacons was proposed. First, a Kalman filter was applied to the signal strength. Then, the path loss index was dynamically calculated using vehicle-to-vehicle communication. Finally, the position of a pedestrian was obtained using weighted centroid localization (WCL) after filtering the nodes. The positioning accuracy was evaluated using a simulator and demonstrated the superiority of the proposed method.
Highlights
In recent years, intelligent transport systems (ITSs) have been actively researched for improving automobile safety and driver comfort
The results show that our method is more effective than the existing methods because the average, maximum, and minimum positioning errors obtained by our method are the smallest, and the convergence speed is the fastest
Our method shows better results than WCL2, which means that filtering nodes enables more accurate and more stable position estimation
Summary
Intelligent transport systems (ITSs) have been actively researched for improving automobile safety and driver comfort. The accuracy of each other’s position information is important. GPS signals can be blocked by buildings and are affected by multipath, which increases the positioning error [3] [4]. Since the accuracy of GPS is affected by the surrounding environment, it is difficult to estimate the position stably. A vehicle can estimate its position with higher accuracy than a pedestrian. In this study, we focus on the positioning accuracy of pedestrians in urban areas and propose a method to improve the accuracy by using the signal strengths from vehicles and beacons
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