Paper Info
Title
A Bayesian Compressive Sensing Vehicular Location Method Based On Three-Dimensional Radio Frequency
Abstract
In vehicular ad hoc networks (VANETs) safety applications, vehicular position is fundamental information to achieve collision avoidance and fleet management. Now, position information is comprehensively provided by global positioning system (GPS). However, in the dense urban, due to multipath effect and signal occlusion, GPS-based positioning method potentially fails to provide accurate position information. For this reason, an assistant approach has been presented in this paper by using three-dimensional radio frequency, such as time of arrival (TOA) and direction of arrival (DOA). With the goal of providing an efficient and reliable estimation of vehicular position in general traffic scenarios, we propose a hybrid TOA/DOA positioning method based on Bayesian compressive sensing (BCS), which benefits from the realization of vehicle-to-roadside wireless interaction with the dedicated short range communication. The effectiveness of the proposed approach is proved through extensive experiments in several scenarios where different signal configurations and the noise conditions are taken into account. Moreover, some comparative experiments are also performed to confirm the strength of our proposed approach.
Year
DOI
Venue
2014
10.1155/2014/483613
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Field
DocType
Volume
Wireless,Computer science,Direction of arrival,Simulation,Real-time computing,Collision,Radio frequency,Global Positioning System,Wireless ad hoc network,Fleet management,Distributed computing,Time of arrival
Journal
2014
ISSN
Citations 
PageRank 
1550-1477
5
0.45
References 
Authors
32
6
Name
Order
Citations
PageRank
Yunpeng Wang119425.34
Xuting Duan2264.92
Daxin Tian320432.49
Jianshan Zhou412913.66
Yingrong Lu591.53
Guangquan Lu6163.72