Analyzing meteoroid flights using particle filters
Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a nov...
| Main Authors: | , , |
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| Format: | Journal Article |
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Institute of Physics Publishing
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/50524 |
| _version_ | 1848758491891630080 |
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| author | Sansom, E. Rutten, M. Bland, Phil |
| author_facet | Sansom, E. Rutten, M. Bland, Phil |
| author_sort | Sansom, E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a novel approach to modeling the uncertainty in meteoroid trajectories and incorporates errors in initial parameters, the dynamical model used, and observed position measurements. Unlike other stochastic approaches, a particle filter does not require predefined values for initial conditions or unobservable trajectory parameters. The Bunburra Rockhole fireball, observed by the Australian Desert Fireball Network (DFN) in 2007, is used to determine the effectiveness of a particle filter for use in fireball trajectory modeling. The final mass is determined to be 2.16 ± 1.33 kg with a final velocity of 6030 ± 216 ms-1, similar to previously calculated values. The full automatability of this approach will allow an unbiased evaluation of all events observed by the DFN and lead to a better understanding of the dynamical state and size frequency distribution of asteroid and cometary debris in the inner solar system. © 2017. The American Astronomical Society. All rights reserved. |
| first_indexed | 2025-11-14T09:44:50Z |
| format | Journal Article |
| id | curtin-20.500.11937-50524 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:44:50Z |
| publishDate | 2017 |
| publisher | Institute of Physics Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-505242017-09-13T15:36:21Z Analyzing meteoroid flights using particle filters Sansom, E. Rutten, M. Bland, Phil Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a novel approach to modeling the uncertainty in meteoroid trajectories and incorporates errors in initial parameters, the dynamical model used, and observed position measurements. Unlike other stochastic approaches, a particle filter does not require predefined values for initial conditions or unobservable trajectory parameters. The Bunburra Rockhole fireball, observed by the Australian Desert Fireball Network (DFN) in 2007, is used to determine the effectiveness of a particle filter for use in fireball trajectory modeling. The final mass is determined to be 2.16 ± 1.33 kg with a final velocity of 6030 ± 216 ms-1, similar to previously calculated values. The full automatability of this approach will allow an unbiased evaluation of all events observed by the DFN and lead to a better understanding of the dynamical state and size frequency distribution of asteroid and cometary debris in the inner solar system. © 2017. The American Astronomical Society. All rights reserved. 2017 Journal Article http://hdl.handle.net/20.500.11937/50524 10.3847/1538-3881/153/2/87 Institute of Physics Publishing fulltext |
| spellingShingle | Sansom, E. Rutten, M. Bland, Phil Analyzing meteoroid flights using particle filters |
| title | Analyzing meteoroid flights using particle filters |
| title_full | Analyzing meteoroid flights using particle filters |
| title_fullStr | Analyzing meteoroid flights using particle filters |
| title_full_unstemmed | Analyzing meteoroid flights using particle filters |
| title_short | Analyzing meteoroid flights using particle filters |
| title_sort | analyzing meteoroid flights using particle filters |
| url | http://hdl.handle.net/20.500.11937/50524 |