Correlation-sum-deviation ranging method for vehicular node based on IEEE 802.11p short preamble. (August 2016)

• Record Type: Journal Article
• Authors: Cui, Xuerong; Gulliver, T Aaron; Zhang, Hao; Li, Juan; Wu, Chunlei
• Is Part Of: International journal of distributed sensor networks. Volume 12:Number 8(2016)
• Publication Date: 2016-08
• Subjects: Vehicle positioning -- ranging -- time of arrival estimation -- IEEE 802.11p -- cross-correlation; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147716660904
• Languages: English
• ISSNs: 1550-1329

Abstract

Growth of the traffic flow and traffic accident has raised more and more demands on wireless communication and positioning technologies that can provide new services such as vehicle collision warning and traffic management. Currently, the global navigation satellite system such as global positioning system and BeiDou satellite positioning system is widely used in vehicles and is fairly accurate in flat open areas. However, the global navigation satellite system can only work in line of sight environment, and it fails to operate in non-line of sight tunnels or downtown areas where blockage of satellite signals is frequent. Because of the shortages of global navigation satellite system, the wireless ranging or positioning system using the short preamble of IEEE 802.11p is provided. Typically, accurate time of arrival estimation is very important for positioning estimation. In order to improve the precision of the time of arrival estimation, a correlation-sum-deviation method for ranging using the IEEE 802.11p short preamble is proposed. Simulation results are presented which show that in both the additive white Gaussian noise channel and the international telecommunications union multipath channel for vehicular environments, the proposed method provides better precision and is less complex than other techniques, particularly when the signal-to-noise ratio is low.