Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association
In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/19/1/112 |
| _version_ | 1811184750110441472 |
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| author | Yuan Huang Taek Lyul Song Dae Hoon Cheagal |
| author_facet | Yuan Huang Taek Lyul Song Dae Hoon Cheagal |
| author_sort | Yuan Huang |
| collection | DOAJ |
| description | In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data association events grows exponentially with the number of measurement cells and the number of tracks. MD-JIPDA is plagued by large increases in computational complexity when targets are closely spaced or move cross each other, especially in multiple detection scenarios. Here, the multiple detection Markov chain joint integrated probabilistic data association (MD-MC-JIPDA) is proposed, in which a Markov chain is used to generate random data association sequences. These sequences are substitutes for the association events. The Markov chain process significantly reduces the computational cost since only a few association sequences are generated while keeping preferable tracking performance. Finally, MD-MC-JIPDA is experimentally validated to demonstrate its effectiveness compared with some of the existing multiple detection data association algorithms. |
| first_indexed | 2024-04-11T13:18:46Z |
| format | Article |
| id | doaj.art-0a1a3f72fc4949b687f9935beb902ff3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:18:46Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0a1a3f72fc4949b687f9935beb902ff32022-12-22T04:22:18ZengMDPI AGSensors1424-82202018-12-0119111210.3390/s19010112s19010112Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data AssociationYuan Huang0Taek Lyul Song1Dae Hoon Cheagal2Department of Electronic Systems Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Electronic Systems Engineering, Hanyang University, Ansan 15588, KoreaLIG System, Seoul 03130, KoreaIn multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data association events grows exponentially with the number of measurement cells and the number of tracks. MD-JIPDA is plagued by large increases in computational complexity when targets are closely spaced or move cross each other, especially in multiple detection scenarios. Here, the multiple detection Markov chain joint integrated probabilistic data association (MD-MC-JIPDA) is proposed, in which a Markov chain is used to generate random data association sequences. These sequences are substitutes for the association events. The Markov chain process significantly reduces the computational cost since only a few association sequences are generated while keeping preferable tracking performance. Finally, MD-MC-JIPDA is experimentally validated to demonstrate its effectiveness compared with some of the existing multiple detection data association algorithms.http://www.mdpi.com/1424-8220/19/1/112Markov chain processmultiple detectiontarget existence evaluationmultitarget trackingdata association |
| spellingShingle | Yuan Huang Taek Lyul Song Dae Hoon Cheagal Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association Sensors Markov chain process multiple detection target existence evaluation multitarget tracking data association |
| title | Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association |
| title_full | Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association |
| title_fullStr | Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association |
| title_full_unstemmed | Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association |
| title_short | Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association |
| title_sort | markov chain realization of multiple detection joint integrated probabilistic data association |
| topic | Markov chain process multiple detection target existence evaluation multitarget tracking data association |
| url | http://www.mdpi.com/1424-8220/19/1/112 |
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