Bayesian Cooperative Localization With NLOS and Malicious Vehicle Detection in GNSS-Challenged Environments

Junhui Zhao,Shanjin Ni,Yinghao Zhang,Qiuping Li

doi:10.1109/access.2020.2992338

Abstract

Owing to the inter-vehicle non-line-of-sight (NLOS) measurement and malicious attack in global navigation satellite system (GNSS) challenged environment, the vehicle position precision is seriously damaged. In order to improve the vehicle position accuracy, we propose a new Bayesian cooperative localization scheme which tackles this problem by combining the vehicle position measurements and inter-vehicle distance measurements. In the proposed scheme, an abnormal vehicle detection algorithm (AVDA) is presented to eliminate the impacts of NLOS and malicious attack. Simulation results demonstrate that the proposed scheme can achieve excellent localization performance in the presence of NLOS and malicious attacks. Based on these results, the abnormal and normal detection rates of AVDA are approximate and the root mean square error (RMSE) is reduced to the sub-meter level. The performances of the proposed scheme are also verified in real environmental conditions by using the simulation of urban mobility (SUMO).

Highlights

Intelligent transportation systems (ITS), which aims at providing innovative services and making safer, more convenient use of traffic network, typically depends on the accurate and reliable vehicle location information [1]
The cooperation in vehicular networking is able to alleviate the shortcomings of Global Navigation Satellite System (GNSS) by incorporating the additional information which is independent of GNSS [6]
BAYESIAN COOPERATIVE LOCALIZATION WITH ABNORMAL VEHICLE DETECTION The proposed scheme aims to update vehicle position using normal vehicle measurement information selected by abnormal vehicle detection algorithm (AVDA)

Summary

INTRODUCTION

Intelligent transportation systems (ITS), which aims at providing innovative services and making safer, more convenient use of traffic network, typically depends on the accurate and reliable vehicle location information [1]. The NLOS link and the malicious attack detection are required to ensure the credibility of localization before using the measured data to update the ego vehicle position. Vehicle nodes in VANET obtain GNSS positioning information through the GNSS receiver, inter-distance information with neighboring vehicles through the range sensor, and exchange these measurements with each other through V2V. Whether as a weight factor or as a data source of data fusion, the accuracy of inter-vehicle distance was the basis of the accuracy of position estimation These studies all assumed that vehicle measured the inter-vehicle distance in ideal vehicular network without NLOS link and malicious attacks. (1h) where p+e (k) and p+n (k) are the estimated position of the ego vehicle and neighboring vehicle, which are updated by the Bayesian cooperative localization method described in subsection IV-C. Where the n-th row of rk , d(k), nk , mk , and wk are rn(k), dn∗(k), nlos(nk), m(nk), and ωn(k), respectively

BAYESIAN COOPERATIVE LOCALIZATION WITH ABNORMAL VEHICLE DETECTION

13: Compute the normalized detection variable Zri

Findings

CONCLUSION