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...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
2021
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| Online Access: | http://hdl.handle.net/20.500.11937/83422 |
| _version_ | 1848764585097560064 |
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| author | Cao, Zhanglong Bryant, David Molteno, Timothy CA Fox, Colin Parry, Matthew |
| author_facet | Cao, Zhanglong Bryant, David Molteno, Timothy CA Fox, Colin Parry, Matthew |
| author_sort | Cao, Zhanglong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:21:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-83422 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:21:41Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-834222021-05-17T03:23:10Z V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction Cao, Zhanglong Bryant, David Molteno, Timothy CA Fox, Colin Parry, Matthew 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. 2021 Journal Article http://hdl.handle.net/20.500.11937/83422 10.3390/s21093215 http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Cao, Zhanglong Bryant, David Molteno, Timothy CA 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 |
| url | http://hdl.handle.net/20.500.11937/83422 |