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V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction
Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline—which we name the V-spline—that incorporates position and velocity information and a penalty term that controls acceleration. We introduce a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125788/ https://www.ncbi.nlm.nih.gov/pubmed/34066396 http://dx.doi.org/10.3390/s21093215 |
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author | Cao, Zhanglong Bryant, David Molteno, Timothy C.A. Fox, Colin Parry, Matthew |
author_facet | Cao, Zhanglong Bryant, David Molteno, Timothy C.A. Fox, Colin Parry, Matthew |
author_sort | Cao, Zhanglong |
collection | PubMed |
description | Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline—which we name the V-spline—that incorporates position and velocity information and a penalty term that controls acceleration. We introduce an adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is proposed, and, in simulation studies, the V-spline shows superior performance to existing methods. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle. |
format | Online Article Text |
id | pubmed-8125788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81257882021-05-17 V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction Cao, Zhanglong Bryant, David Molteno, Timothy C.A. Fox, Colin Parry, Matthew Sensors (Basel) Article Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline—which we name the V-spline—that incorporates position and velocity information and a penalty term that controls acceleration. We introduce an adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is proposed, and, in simulation studies, the V-spline shows superior performance to existing methods. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle. MDPI 2021-05-06 /pmc/articles/PMC8125788/ /pubmed/34066396 http://dx.doi.org/10.3390/s21093215 Text en © 2021 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 Cao, Zhanglong Bryant, David Molteno, Timothy C.A. Fox, Colin Parry, Matthew V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction |
title | V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction |
title_full | V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction |
title_fullStr | V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction |
title_full_unstemmed | V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction |
title_short | V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction |
title_sort | v-spline: an adaptive smoothing spline for trajectory reconstruction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125788/ https://www.ncbi.nlm.nih.gov/pubmed/34066396 http://dx.doi.org/10.3390/s21093215 |
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