Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking
Gaussian mixture probability hypothesis density (GM-PHD) filtering often assumes a uniform distribution of clutter in the observation area. However, in practice, clutter is often unknown and non-uniform, necessitating accurate estimation of its spatial distribution, non-uniformity, and temporal...
| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Instituto de Aeronáutica e Espaço (IAE)
2024-04-01
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| シリーズ: | Journal of Aerospace Technology and Management |
| 主題: | |
| オンライン･アクセス: | https://jatm.com.br/jatm/article/view/1325 |
| _version_ | 1827281568862830592 |
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| author | Lifan Sun Wenhui Xue Dan Gao |
| author_facet | Lifan Sun Wenhui Xue Dan Gao |
| author_sort | Lifan Sun |
| collection | DOAJ |
| description |
Gaussian mixture probability hypothesis density (GM-PHD) filtering often assumes a uniform distribution of clutter in the observation area. However, in practice, clutter is often unknown and non-uniform, necessitating accurate estimation of its spatial distribution, non-uniformity, and temporal variations. To address this problem, we proposed a modified GM-PHD filtering method with clutter density estimation for multiple target tracking. In the proposed method, first, potential target measurements within the tracking gate are eliminated to obtain the clutter measurement set. Next, the clutter density around each target is estimated. Finally, the estimated clutter density is incorporated into GM-PHD filtering, to estimate the target state and clutter density in complex clutter environments. Simulation results demonstrated that the proposed filtering method improves the performance of the GM-PHD filter in multi-target tracking scenarios with unknown clutter density.
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| first_indexed | 2024-04-24T09:02:24Z |
| format | Article |
| id | doaj.art-f1987043b5f944c89c195dd76b50420c |
| institution | Directory Open Access Journal |
| issn | 2175-9146 |
| language | English |
| last_indexed | 2024-04-24T09:02:24Z |
| publishDate | 2024-04-01 |
| publisher | Instituto de Aeronáutica e Espaço (IAE) |
| record_format | Article |
| series | Journal of Aerospace Technology and Management |
| spelling | doaj.art-f1987043b5f944c89c195dd76b50420c2024-04-15T20:55:10ZengInstituto de Aeronáutica e Espaço (IAE)Journal of Aerospace Technology and Management2175-91462024-04-0116Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target TrackingLifan Sun0Wenhui Xue1Dan Gao2Henan University of Science and Technology – School of Information Engineering – Luoyang/Honã – China | Longmen Laboratory – Luoyang/Honã – ChinaHenan University of Science and Technology – School of Information Engineering – Luoyang/Honã – China Henan University of Science and Technology – School of Information Engineering – Luoyang/Honã – China Gaussian mixture probability hypothesis density (GM-PHD) filtering often assumes a uniform distribution of clutter in the observation area. However, in practice, clutter is often unknown and non-uniform, necessitating accurate estimation of its spatial distribution, non-uniformity, and temporal variations. To address this problem, we proposed a modified GM-PHD filtering method with clutter density estimation for multiple target tracking. In the proposed method, first, potential target measurements within the tracking gate are eliminated to obtain the clutter measurement set. Next, the clutter density around each target is estimated. Finally, the estimated clutter density is incorporated into GM-PHD filtering, to estimate the target state and clutter density in complex clutter environments. Simulation results demonstrated that the proposed filtering method improves the performance of the GM-PHD filter in multi-target tracking scenarios with unknown clutter density. https://jatm.com.br/jatm/article/view/1325Gaussian mixture probability hypothesis densityComplex clutter environments Clutter density estimation |
| spellingShingle | Lifan Sun Wenhui Xue Dan Gao Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking Journal of Aerospace Technology and Management Gaussian mixture probability hypothesis density Complex clutter environments Clutter density estimation |
| title | Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking |
| title_full | Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking |
| title_fullStr | Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking |
| title_full_unstemmed | Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking |
| title_short | Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking |
| title_sort | modified gaussian mixture probability hypothesis density filtering using clutter density estimation for multiple target tracking |
| topic | Gaussian mixture probability hypothesis density Complex clutter environments Clutter density estimation |
| url | https://jatm.com.br/jatm/article/view/1325 |
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