A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation
Abstract This paper proposes a novel joint decision and estimation (JDE) solution for the multi-target detection and tracking (MDT) problem. MDT aims to jointly detect the number of targets and estimate their states, which is essentially a JDE problem since detection and tracking are highly coupled....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-06-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | https://doi.org/10.1186/s13634-023-01034-x |
| _version_ | 1797795487512788992 |
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| author | Wen Cao Qiwei Li |
| author_facet | Wen Cao Qiwei Li |
| author_sort | Wen Cao |
| collection | DOAJ |
| description | Abstract This paper proposes a novel joint decision and estimation (JDE) solution for the multi-target detection and tracking (MDT) problem. MDT aims to jointly detect the number of targets and estimate their states, which is essentially a JDE problem since detection and tracking are highly coupled. Thus, a joint solution which can utilize the coupling is preferable. However, the existing JDE approach has either poor performance or excessive design parameters without considering the MDT problem realities, i.e., the losses that different decisions may lead to. Therefore, we propose a compact conditional JDE (CCJDE)-based MDT method with less design parameters but superior performance. Specifically, we propose a CCJDE-based MDT risk which unifies the detection and tracking risks in a compact way. Then, we derive the joint detection and tracking solution accounting for their couplings, where the joint probabilistic data association filter is adopted due to its advantageous performance and the adaptability to the JDE framework. Then, an efficient CCJDE-MDT algorithm is developed. Besides, some parameter designing guidelines are presented by considering the MDT realities. Simulation results verify the effectiveness of the proposed CCJDE-MDT method, which outperforms the traditional decision-then-estimation in joint performance and also beats the existing recursive joint decision and estimation(RJDE) method in many cases. |
| first_indexed | 2024-03-13T03:18:42Z |
| format | Article |
| id | doaj.art-fa5170bf697443929ccae2318fb101ba |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-03-13T03:18:42Z |
| publishDate | 2023-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-fa5170bf697443929ccae2318fb101ba2023-06-25T11:32:11ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802023-06-012023112210.1186/s13634-023-01034-xA novel joint multi-target detection and tracking approach based on Bayes joint decision and estimationWen Cao0Qiwei Li1School of Electronics and Control Engineering, Chang’an UniversitySchool of Electronics and Information, Northwestern Polytechnical UniversityAbstract This paper proposes a novel joint decision and estimation (JDE) solution for the multi-target detection and tracking (MDT) problem. MDT aims to jointly detect the number of targets and estimate their states, which is essentially a JDE problem since detection and tracking are highly coupled. Thus, a joint solution which can utilize the coupling is preferable. However, the existing JDE approach has either poor performance or excessive design parameters without considering the MDT problem realities, i.e., the losses that different decisions may lead to. Therefore, we propose a compact conditional JDE (CCJDE)-based MDT method with less design parameters but superior performance. Specifically, we propose a CCJDE-based MDT risk which unifies the detection and tracking risks in a compact way. Then, we derive the joint detection and tracking solution accounting for their couplings, where the joint probabilistic data association filter is adopted due to its advantageous performance and the adaptability to the JDE framework. Then, an efficient CCJDE-MDT algorithm is developed. Besides, some parameter designing guidelines are presented by considering the MDT realities. Simulation results verify the effectiveness of the proposed CCJDE-MDT method, which outperforms the traditional decision-then-estimation in joint performance and also beats the existing recursive joint decision and estimation(RJDE) method in many cases.https://doi.org/10.1186/s13634-023-01034-x |
| spellingShingle | Wen Cao Qiwei Li A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation EURASIP Journal on Advances in Signal Processing |
| title | A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation |
| title_full | A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation |
| title_fullStr | A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation |
| title_full_unstemmed | A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation |
| title_short | A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation |
| title_sort | novel joint multi target detection and tracking approach based on bayes joint decision and estimation |
| url | https://doi.org/10.1186/s13634-023-01034-x |
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