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一种基于车辆无线网络中偏度分析的定时估计方法。

A Timing Estimation Method Based-on Skewness Analysis in Vehicular Wireless Networks.

作者信息

Cui Xuerong, Li Juan, Wu Chunlei, Liu Jian-Hang

机构信息

Department of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao 266580, China.

出版信息

Sensors (Basel). 2015 Nov 13;15(11):28942-59. doi: 10.3390/s151128942.

Abstract

Vehicle positioning technology has drawn more and more attention in vehicular wireless networks to reduce transportation time and traffic accidents. Nowadays, global navigation satellite systems (GNSS) are widely used in land vehicle positioning, but most of them are lack precision and reliability in situations where their signals are blocked. Positioning systems base-on short range wireless communication are another effective way that can be used in vehicle positioning or vehicle ranging. IEEE 802.11p is a new real-time short range wireless communication standard for vehicles, so a new method is proposed to estimate the time delay or ranges between vehicles based on the IEEE 802.11p standard which includes three main steps: cross-correlation between the received signal and the short preamble, summing up the correlated results in groups, and finding the maximum peak using a dynamic threshold based on the skewness analysis. With the range between each vehicle or road-side infrastructure, the position of neighboring vehicles can be estimated correctly. Simulation results were presented in the International Telecommunications Union (ITU) vehicular multipath channel, which show that the proposed method provides better precision than some well-known timing estimation techniques, especially in low signal to noise ratio (SNR) environments.

摘要

车辆定位技术在车载无线网络中越来越受到关注,以减少运输时间和交通事故。如今,全球导航卫星系统(GNSS)广泛应用于陆地车辆定位,但在信号被阻挡的情况下,大多数系统缺乏精度和可靠性。基于短程无线通信的定位系统是可用于车辆定位或车辆测距的另一种有效方法。IEEE 802.11p是一种新的车辆实时短程无线通信标准,因此提出了一种基于IEEE 802.11p标准估计车辆间时间延迟或距离的新方法,该方法包括三个主要步骤:接收信号与短前导码的互相关、对相关结果进行分组求和以及基于偏度分析使用动态阈值找到最大峰值。利用每辆车与路边基础设施之间的距离,可以正确估计相邻车辆的位置。在国际电信联盟(ITU)的车载多径信道中给出了仿真结果,结果表明所提方法比一些知名的定时估计技术具有更高的精度,尤其是在低信噪比(SNR)环境中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/371e/4701315/4b33f64662e0/sensors-15-28942-g001.jpg

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