The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection
Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Mar...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1508 |
| _version_ | 1797263862969401344 |
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| author | Zhixuan Xu Yu Wei Xiaobao Qin Pengfei Guo |
| author_facet | Zhixuan Xu Yu Wei Xiaobao Qin Pengfei Guo |
| author_sort | Zhixuan Xu |
| collection | DOAJ |
| description | Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target state of multiple motion models without knowing the current motion models. The performances of the generalized covariance intersection (GCI)GCI-GM-JMNS-CPHD and generalized inverse covariance intersection (GICI)GICI-GM-JMNS-CPHD methods are evaluated via simulation results. The simulation results show that, compared with algorithms such as Sensor1, Sensor2, GCI-GM-CPHD, and GICI-GM-CPHD, this algorithm has smaller optimal subpattern assignment (OSPA) errors and a higher fusion accuracy. |
| first_indexed | 2024-04-25T00:19:45Z |
| format | Article |
| id | doaj.art-2487c369ed2d492da6054bb53a94eab5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:19:45Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2487c369ed2d492da6054bb53a94eab52024-03-12T16:55:02ZengMDPI AGSensors1424-82202024-02-01245150810.3390/s24051508The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance IntersectionZhixuan Xu0Yu Wei1Xiaobao Qin2Pengfei Guo3School of Mathematics and Statistics, Hainan University, Haikou 570000, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570000, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570000, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570000, ChinaSome fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target state of multiple motion models without knowing the current motion models. The performances of the generalized covariance intersection (GCI)GCI-GM-JMNS-CPHD and generalized inverse covariance intersection (GICI)GICI-GM-JMNS-CPHD methods are evaluated via simulation results. The simulation results show that, compared with algorithms such as Sensor1, Sensor2, GCI-GM-CPHD, and GICI-GM-CPHD, this algorithm has smaller optimal subpattern assignment (OSPA) errors and a higher fusion accuracy.https://www.mdpi.com/1424-8220/24/5/1508generalized inverse covariance intersectionjumping MarkovGM-CPHDnonlinear motion tracking |
| spellingShingle | Zhixuan Xu Yu Wei Xiaobao Qin Pengfei Guo The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection Sensors generalized inverse covariance intersection jumping Markov GM-CPHD nonlinear motion tracking |
| title | The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection |
| title_full | The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection |
| title_fullStr | The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection |
| title_full_unstemmed | The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection |
| title_short | The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection |
| title_sort | gm jmns cphd filtering algorithm for nonlinear systems based on a generalized covariance intersection |
| topic | generalized inverse covariance intersection jumping Markov GM-CPHD nonlinear motion tracking |
| url | https://www.mdpi.com/1424-8220/24/5/1508 |
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