Gaussian mixture importance sampling function for unscented SMC-PHD filter
The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments. However, the USMC-PHD filter suffers from heavy computational complexity because the unscented inform...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/56017 |
| _version_ | 1848759765202632704 |
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| author | Yoon, J. Kim, Du Yong Yoon, K. |
| author_facet | Yoon, J. Kim, Du Yong Yoon, K. |
| author_sort | Yoon, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments. However, the USMC-PHD filter suffers from heavy computational complexity because the unscented information filter is assigned for every particle to approximate an importance sampling function. In this paper, we propose a Gaussian mixture form of the importance sampling function for the SMC-PHD filter to considerably reduce the computational complexity without performance degradation. Simulation results support that the proposed importance sampling function is effective in computational aspects compared with variants of SMC-PHD filters and competitive to the USMC-PHD filter in accuracy. © 2013 Elsevier B.V. All rights reserved. |
| first_indexed | 2025-11-14T10:05:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-56017 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:05:05Z |
| publishDate | 2013 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-560172017-09-13T16:11:02Z Gaussian mixture importance sampling function for unscented SMC-PHD filter Yoon, J. Kim, Du Yong Yoon, K. The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments. However, the USMC-PHD filter suffers from heavy computational complexity because the unscented information filter is assigned for every particle to approximate an importance sampling function. In this paper, we propose a Gaussian mixture form of the importance sampling function for the SMC-PHD filter to considerably reduce the computational complexity without performance degradation. Simulation results support that the proposed importance sampling function is effective in computational aspects compared with variants of SMC-PHD filters and competitive to the USMC-PHD filter in accuracy. © 2013 Elsevier B.V. All rights reserved. 2013 Journal Article http://hdl.handle.net/20.500.11937/56017 10.1016/j.sigpro.2013.03.004 Elsevier BV restricted |
| spellingShingle | Yoon, J. Kim, Du Yong Yoon, K. Gaussian mixture importance sampling function for unscented SMC-PHD filter |
| title | Gaussian mixture importance sampling function for unscented SMC-PHD filter |
| title_full | Gaussian mixture importance sampling function for unscented SMC-PHD filter |
| title_fullStr | Gaussian mixture importance sampling function for unscented SMC-PHD filter |
| title_full_unstemmed | Gaussian mixture importance sampling function for unscented SMC-PHD filter |
| title_short | Gaussian mixture importance sampling function for unscented SMC-PHD filter |
| title_sort | gaussian mixture importance sampling function for unscented smc-phd filter |
| url | http://hdl.handle.net/20.500.11937/56017 |