Efficient importance sampling function design for sequential Monte Carlo PHD filter
In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS)...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
Elsevier BV
2012
|
| Online Access: | http://hdl.handle.net/20.500.11937/56383 |
| _version_ | 1848759861440937984 |
|---|---|
| author | Hong Yoon, J. Kim, Du Yong Yoon, K. |
| author_facet | Hong Yoon, J. Kim, Du Yong Yoon, K. |
| author_sort | Hong Yoon, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function. Even though this filter permits general class of IS density function, many previous implementations have simply used the state transition density function. However, this approach leads to a degeneracy problem and renders the filter inefficient. Thus, we propose a novel IS function for the SMC-PHD filter using a combination of an unscented information filter and a gating technique. Further, we use measurement-driven birth target intensities because they are more efficient and accurate than selecting birth targets selected using arbitrary or expected mean target states. The performance of the SMC-PHD filter with the proposed IS function was subsequently evaluated through a simulation and it was shown to outperform the standard SMC-PHD filter and recently proposed auxiliary PHD filter. © 2012 Elsevier B.V. All rights reserved. |
| first_indexed | 2025-11-14T10:06:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-56383 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:06:36Z |
| publishDate | 2012 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-563832017-09-13T16:10:39Z Efficient importance sampling function design for sequential Monte Carlo PHD filter Hong Yoon, J. Kim, Du Yong Yoon, K. In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function. Even though this filter permits general class of IS density function, many previous implementations have simply used the state transition density function. However, this approach leads to a degeneracy problem and renders the filter inefficient. Thus, we propose a novel IS function for the SMC-PHD filter using a combination of an unscented information filter and a gating technique. Further, we use measurement-driven birth target intensities because they are more efficient and accurate than selecting birth targets selected using arbitrary or expected mean target states. The performance of the SMC-PHD filter with the proposed IS function was subsequently evaluated through a simulation and it was shown to outperform the standard SMC-PHD filter and recently proposed auxiliary PHD filter. © 2012 Elsevier B.V. All rights reserved. 2012 Journal Article http://hdl.handle.net/20.500.11937/56383 10.1016/j.sigpro.2012.01.010 Elsevier BV restricted |
| spellingShingle | Hong Yoon, J. Kim, Du Yong Yoon, K. Efficient importance sampling function design for sequential Monte Carlo PHD filter |
| title | Efficient importance sampling function design for sequential Monte Carlo PHD filter |
| title_full | Efficient importance sampling function design for sequential Monte Carlo PHD filter |
| title_fullStr | Efficient importance sampling function design for sequential Monte Carlo PHD filter |
| title_full_unstemmed | Efficient importance sampling function design for sequential Monte Carlo PHD filter |
| title_short | Efficient importance sampling function design for sequential Monte Carlo PHD filter |
| title_sort | efficient importance sampling function design for sequential monte carlo phd filter |
| url | http://hdl.handle.net/20.500.11937/56383 |