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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 1/2022

08.02.2022

Improved RSS Based Distance Estimation for Autonomous Vehicles

verfasst von: Gokce Hacioglu, Erhan Sesli

Erschienen in: Wireless Personal Communications | Ausgabe 1/2022

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Abstract

Autonomous vehicles are going to be used in warehouses or logistic centers more frequently in near future. The location information is vital for autonomous vehicles to accomplish tasks that are assigned to them. This study presents a wireless sensor network to be used in location estimation of autonomous vehicles. The autonomous vehicles estimate their distance to a specific node called as reference anchor node. The aim of the proposed method is to be able get more accurate distance estimations by received signal strength for autonomous vehicles. The proposed wireless sensor network provides sufficient information to the autonomous vehicles to reduce their received signal strength based estimation error. An adaptive filter based algorithm to reduce estimation error is proposed. The performance of the proposed method is validated by simulations and experiments. According to results of the simulations where ideal conditions are provided, maximum error of the proposed method is 0.81m. According to results of the experiments, the average absolute error of the proposed method can be as low as 1.272m. When the proposed method is compared with k-nearest neighbor distance estimation and conventional approach, it has a significantly lower error than them.
Vorheriger Artikel An Approach for Intersection Prevention in Transmission Channel Using Correlation Matrix and Tree Construction Model in V2X Framework
Nächster Artikel Learning-Based Multi Attribute Network Selection in Heterogeneous Wireless Access

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Metadaten
Titel
Improved RSS Based Distance Estimation for Autonomous Vehicles
verfasst von
Gokce Hacioglu
Erhan Sesli
Publikationsdatum
08.02.2022
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 1/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09552-x

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