A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/483613 |
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| _version_ | 1832547874104147968 |
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| author | Yunpeng Wang Xuting Duan Daxin Tian Jianshan Zhou Yingrong Lu Guangquan Lu |
| author_facet | Yunpeng Wang Xuting Duan Daxin Tian Jianshan Zhou Yingrong Lu Guangquan Lu |
| author_sort | Yunpeng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ca54e0cbed534d8f8c42cd1480f8b1bc |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ca54e0cbed534d8f8c42cd1480f8b1bc2025-02-03T06:43:05ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-06-011010.1155/2014/483613483613A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio FrequencyYunpeng Wang0Xuting Duan1Daxin Tian2Jianshan Zhou3Yingrong Lu4Guangquan Lu5 Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security, Beijing 100191, China Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security, Beijing 100191, China Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security, Beijing 100191, China Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security, Beijing 100191, China Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security, Beijing 100191, China Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security, Beijing 100191, ChinaIn 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.https://doi.org/10.1155/2014/483613 |
| spellingShingle | Yunpeng Wang Xuting Duan Daxin Tian Jianshan Zhou Yingrong Lu Guangquan Lu A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency International Journal of Distributed Sensor Networks |
| title | A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency |
| title_full | A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency |
| title_fullStr | A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency |
| title_full_unstemmed | A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency |
| title_short | A Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency |
| title_sort | bayesian compressive sensing vehicular location method based on three dimensional radio frequency |
| url | https://doi.org/10.1155/2014/483613 |
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