A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency

Yunpeng Wang, Xuting Duan, Daxin Tian, Jianshan Zhou, Yingrong Lu and Guangquan Lu

International Journal of Distributed Sensor Networks, 2014, vol. 10, issue 6, 483613

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:10:y:2014:i:6:p:483613

DOI: 10.1155/2014/483613

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