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 in...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
2018
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/1803.07184 http://hdl.handle.net/20.500.11937/78087 |
| _version_ | 1848763932166062080 |
|---|---|
| author | Cao, Zhanglong Bryant, David Molteno, Tim Fox, Colin Parry, Matthew |
| author_facet | Cao, Zhanglong Bryant, David Molteno, Tim 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 a
particular 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 given and we detail the performance
of the V-spline on four particularly challenging test datasets. 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 | 2025-11-14T11:11:19Z |
| format | Journal Article |
| id | curtin-20.500.11937-78087 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:11:19Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-780872020-07-24T05:18:29Z Adaptive Smoothing Spline for Trajectory Reconstruction Cao, Zhanglong Bryant, David Molteno, Tim Fox, Colin Parry, Matthew stat.ME stat.ME 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 particular 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 given and we detail the performance of the V-spline on four particularly challenging test datasets. 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/78087 https://arxiv.org/abs/1803.07184 fulltext |
| spellingShingle | stat.ME stat.ME Cao, Zhanglong Bryant, David Molteno, Tim Fox, Colin Parry, Matthew Adaptive Smoothing Spline for Trajectory Reconstruction |
| title | Adaptive Smoothing Spline for Trajectory Reconstruction |
| title_full | Adaptive Smoothing Spline for Trajectory Reconstruction |
| title_fullStr | Adaptive Smoothing Spline for Trajectory Reconstruction |
| title_full_unstemmed | Adaptive Smoothing Spline for Trajectory Reconstruction |
| title_short | Adaptive Smoothing Spline for Trajectory Reconstruction |
| title_sort | adaptive smoothing spline for trajectory reconstruction |
| topic | stat.ME stat.ME |
| url | https://arxiv.org/abs/1803.07184 http://hdl.handle.net/20.500.11937/78087 |