Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection
In this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking applications. Covariance intersection (CI) is a well-k...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9468680/ |
| _version_ | 1811210754205941760 |
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| author | Woo Jung Park Chan Gook Park |
| author_facet | Woo Jung Park Chan Gook Park |
| author_sort | Woo Jung Park |
| collection | DOAJ |
| description | In this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking applications. Covariance intersection (CI) is a well-known fusion method that produces a conservative estimate of the joint covariance regardless of the actual correlation between the different nodes. Inverse covariance intersection (ICI) is the updated version to obtain fusion results that guarantee consistency and less conservative than CI. However, the ICI is not extended to multi-sensor multi-target tracking system yet. Since the ICI formula can be re-structured as naïve fusion with covariance inflation in Gaussian pdf, this method was applied to the GM-CPHD with generalization. The formula for random finite set (RFS) fusion was derived in the same way as the conventional generalized covariance intersection (GCI) based fusion. The simulation results for multi-target tracking show that the proposed algorithm has smaller optimal sub-pattern assignment (OSPA) errors than naïve fusion and the GCI-based fusions. |
| first_indexed | 2024-04-12T05:00:58Z |
| format | Article |
| id | doaj.art-0eec6acb75704d2896ebc4f1ce94ea7c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T05:00:58Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0eec6acb75704d2896ebc4f1ce94ea7c2022-12-22T03:47:00ZengIEEEIEEE Access2169-35362021-01-019940789408610.1109/ACCESS.2021.30937199468680Distributed GM-CPHD Filter Based on Generalized Inverse Covariance IntersectionWoo Jung Park0https://orcid.org/0000-0002-0140-749XChan Gook Park1https://orcid.org/0000-0002-7403-951XDepartment of Mechanical and Aerospace Engineering / Automation and System Research Institute, Seoul National University, Seoul, Republic of KoreaDepartment of Mechanical and Aerospace Engineering / Institute of Advanced Aerospace Technology, Seoul National University, Seoul, Republic of KoreaIn this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking applications. Covariance intersection (CI) is a well-known fusion method that produces a conservative estimate of the joint covariance regardless of the actual correlation between the different nodes. Inverse covariance intersection (ICI) is the updated version to obtain fusion results that guarantee consistency and less conservative than CI. However, the ICI is not extended to multi-sensor multi-target tracking system yet. Since the ICI formula can be re-structured as naïve fusion with covariance inflation in Gaussian pdf, this method was applied to the GM-CPHD with generalization. The formula for random finite set (RFS) fusion was derived in the same way as the conventional generalized covariance intersection (GCI) based fusion. The simulation results for multi-target tracking show that the proposed algorithm has smaller optimal sub-pattern assignment (OSPA) errors than naïve fusion and the GCI-based fusions.https://ieeexplore.ieee.org/document/9468680/Multi-target trackingGM-CPHD filterinverse covariance intersectioncovariance inflation |
| spellingShingle | Woo Jung Park Chan Gook Park Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection IEEE Access Multi-target tracking GM-CPHD filter inverse covariance intersection covariance inflation |
| title | Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection |
| title_full | Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection |
| title_fullStr | Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection |
| title_full_unstemmed | Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection |
| title_short | Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection |
| title_sort | distributed gm cphd filter based on generalized inverse covariance intersection |
| topic | Multi-target tracking GM-CPHD filter inverse covariance intersection covariance inflation |
| url | https://ieeexplore.ieee.org/document/9468680/ |
| work_keys_str_mv | AT woojungpark distributedgmcphdfilterbasedongeneralizedinversecovarianceintersection AT changookpark distributedgmcphdfilterbasedongeneralizedinversecovarianceintersection |