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Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an ex...

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Autores principales: Paszek, Krzysztof, Grzechca, Damian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575451/
https://www.ncbi.nlm.nih.gov/pubmed/37837099
http://dx.doi.org/10.3390/s23198270
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author Paszek, Krzysztof
Grzechca, Damian
author_facet Paszek, Krzysztof
Grzechca, Damian
author_sort Paszek, Krzysztof
collection PubMed
description Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an excellent alternative to the global positioning system (GPS) in a limited area but has some drawbacks. Despite many advantages of various object positioning systems, none is free from the problem of object displacement during measurement (data acquisition), which affects positioning accuracy. In addition, temporarily missing data from the absolute positioning system can lead to dangerous situations. Moreover, data pre-processing is unavoidable and takes some time, affecting additionally the object’s displacement in relation to its previous position and its starting point of the new positioning process. So, the prediction of the position of an object is necessary to minimize the time when the position is unknown or out of date, especially when the object is moving at high speed and the position update rate is low. This article proposes using the long short-term memory (LSTM) artificial neural network to predict objects’ positions based on historical data from the UWB system and inertial navigation. The proposed solution creates a reliable positioning system that predicts 10 positions of low and high-speed moving objects with an error below 10 cm. Position prediction allows detection of possible collisions—the intersection of the trajectories of moving objects.
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spelling pubmed-105754512023-10-14 Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects Paszek, Krzysztof Grzechca, Damian Sensors (Basel) Article Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an excellent alternative to the global positioning system (GPS) in a limited area but has some drawbacks. Despite many advantages of various object positioning systems, none is free from the problem of object displacement during measurement (data acquisition), which affects positioning accuracy. In addition, temporarily missing data from the absolute positioning system can lead to dangerous situations. Moreover, data pre-processing is unavoidable and takes some time, affecting additionally the object’s displacement in relation to its previous position and its starting point of the new positioning process. So, the prediction of the position of an object is necessary to minimize the time when the position is unknown or out of date, especially when the object is moving at high speed and the position update rate is low. This article proposes using the long short-term memory (LSTM) artificial neural network to predict objects’ positions based on historical data from the UWB system and inertial navigation. The proposed solution creates a reliable positioning system that predicts 10 positions of low and high-speed moving objects with an error below 10 cm. Position prediction allows detection of possible collisions—the intersection of the trajectories of moving objects. MDPI 2023-10-06 /pmc/articles/PMC10575451/ /pubmed/37837099 http://dx.doi.org/10.3390/s23198270 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Paszek, Krzysztof
Grzechca, Damian
Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects
title Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects
title_full Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects
title_fullStr Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects
title_full_unstemmed Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects
title_short Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects
title_sort using the lstm neural network and the uwb positioning system to predict the position of low and high speed moving objects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575451/
https://www.ncbi.nlm.nih.gov/pubmed/37837099
http://dx.doi.org/10.3390/s23198270
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