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Modified extended object tracker for 2D lidar data using random matrix model

The random matrix (RM) model is a typical extended object-modeling method that has been widely used in extended object tracking. However, existing RM-based filters usually assume that the measurements follow a Gaussian distribution, which may lead to a decrease in accuracy when the filter is applied...

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Autores principales: Li, Peng, Chen, Cheng, You, Cong-zhe, Qiu, Jun-da
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060379/
https://www.ncbi.nlm.nih.gov/pubmed/36991153
http://dx.doi.org/10.1038/s41598-023-32236-w
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author Li, Peng
Chen, Cheng
You, Cong-zhe
Qiu, Jun-da
author_facet Li, Peng
Chen, Cheng
You, Cong-zhe
Qiu, Jun-da
author_sort Li, Peng
collection PubMed
description The random matrix (RM) model is a typical extended object-modeling method that has been widely used in extended object tracking. However, existing RM-based filters usually assume that the measurements follow a Gaussian distribution, which may lead to a decrease in accuracy when the filter is applied to the lidar system. In this paper, a new observation model used to modify an RM smoother by considering the characteristics of 2D LiDAR data is proposed. Simulation results show that the proposed method achieves a better performance than the original RM tracker in a 2D lidar system.
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spelling pubmed-100603792023-03-31 Modified extended object tracker for 2D lidar data using random matrix model Li, Peng Chen, Cheng You, Cong-zhe Qiu, Jun-da Sci Rep Article The random matrix (RM) model is a typical extended object-modeling method that has been widely used in extended object tracking. However, existing RM-based filters usually assume that the measurements follow a Gaussian distribution, which may lead to a decrease in accuracy when the filter is applied to the lidar system. In this paper, a new observation model used to modify an RM smoother by considering the characteristics of 2D LiDAR data is proposed. Simulation results show that the proposed method achieves a better performance than the original RM tracker in a 2D lidar system. Nature Publishing Group UK 2023-03-29 /pmc/articles/PMC10060379/ /pubmed/36991153 http://dx.doi.org/10.1038/s41598-023-32236-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Peng
Chen, Cheng
You, Cong-zhe
Qiu, Jun-da
Modified extended object tracker for 2D lidar data using random matrix model
title Modified extended object tracker for 2D lidar data using random matrix model
title_full Modified extended object tracker for 2D lidar data using random matrix model
title_fullStr Modified extended object tracker for 2D lidar data using random matrix model
title_full_unstemmed Modified extended object tracker for 2D lidar data using random matrix model
title_short Modified extended object tracker for 2D lidar data using random matrix model
title_sort modified extended object tracker for 2d lidar data using random matrix model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060379/
https://www.ncbi.nlm.nih.gov/pubmed/36991153
http://dx.doi.org/10.1038/s41598-023-32236-w
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