Research on autocorrelation and cross-correlation analyses in vehicular nodes positioning

Xuerong Cui,Jianhang Liu,Jingzhen Li,Tingpei Huang,Haihua Chen,Juan Li

doi:10.1177/1550147719843864

Xuerong Cui, Jianhang Liu + Show 4 more

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https://doi.org/10.1177/1550147719843864

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Abstract

In recent years, the massive increase in car ownership has led to a dramatic increase of traffic accidents, especially in the case of multi-vehicle chain collisions. However, most researches of col...

Highlights

According to the report in 2018 by World Health Organization (WHO),[1] even if road traffic accident has decreased, deaths remain unacceptably high at 1.35 million per year, which has become the dominant factor for young people aged from 5 to 29 years old
Critical location techniques in vehicular ad hoc networks (VANETs) based on global navigation satellite systems (GNSS) have been developed, containing receivers for radio, digital map information matching,[9] vehicle models, and motion sensors
The main contribution of this article is to propose a new timing offset estimation or ranging method to alleviate the influence of low signal noise ratio (SNR) and multipath environment in VANET

Summary

Introduction

According to the report in 2018 by World Health Organization (WHO),[1] even if road traffic accident has decreased, deaths remain unacceptably high at 1.35 million per year, which has become the dominant factor for young people aged from 5 to 29 years old. The main contribution of this article is to propose a new timing offset estimation or ranging method to alleviate the influence of low signal noise ratio (SNR) and multipath environment in VANET. We proposed a multi-stage method to enhance timing estimation performance, wherein autocorrelation and cross-correlation are both used for time estimation metric, and iterative update to mean absolute deviation (MAD) values is proposed to find the correct time offset.

Results

Conclusion