Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman
The sequential Monte Carlo cardinality balanced multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with the multi-target tracking problem. However, the standard SMC-CBMeMBer filter suffers from the particles’ degradation problem seriously. Using the measurements to construct the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-09-01
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| Series: | The Journal of Engineering |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0670 |
| _version_ | 1819291314622038016 |
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| author | Haihuan Wang Xiaoyong Lyu Long Ma |
| author_facet | Haihuan Wang Xiaoyong Lyu Long Ma |
| author_sort | Haihuan Wang |
| collection | DOAJ |
| description | The sequential Monte Carlo cardinality balanced multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with the multi-target tracking problem. However, the standard SMC-CBMeMBer filter suffers from the particles’ degradation problem seriously. Using the measurements to construct the proposal density in the step of predict can effectively solve the above problem, but this kind of approach brings an amount of computation and causes the overestimation of the target number. To examine the quality of each predicted particle adaptively and use the cubature Kalman filter (CKF) to refine the poor-quality particles with the aid of the current measurements is proposed in this study. This method manages to alleviate the particles degradation problem without increasing the computational complexity seriously since only a part of the particles is refined by the CKF. Also, the proposed method can avoid cardinality overestimation caused by abuse of measurements. A range of simulations is performed to test the performance of the proposed method. The results confirm the effectiveness and robustness of the novel method. |
| first_indexed | 2024-12-24T03:36:40Z |
| format | Article |
| id | doaj.art-799c648f77de43f9b32209bc2480e5b3 |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-12-24T03:36:40Z |
| publishDate | 2019-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-799c648f77de43f9b32209bc2480e5b32022-12-21T17:17:02ZengWileyThe Journal of Engineering2051-33052019-09-0110.1049/joe.2019.0670JOE.2019.0670Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature KalmanHaihuan Wang0Xiaoyong Lyu1Long Ma2East China Institute of Electronic EngineeringSchool of Information Engineering, Zhengzhou UniversitySchool of Information Engineering, Zhengzhou UniversityThe sequential Monte Carlo cardinality balanced multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with the multi-target tracking problem. However, the standard SMC-CBMeMBer filter suffers from the particles’ degradation problem seriously. Using the measurements to construct the proposal density in the step of predict can effectively solve the above problem, but this kind of approach brings an amount of computation and causes the overestimation of the target number. To examine the quality of each predicted particle adaptively and use the cubature Kalman filter (CKF) to refine the poor-quality particles with the aid of the current measurements is proposed in this study. This method manages to alleviate the particles degradation problem without increasing the computational complexity seriously since only a part of the particles is refined by the CKF. Also, the proposed method can avoid cardinality overestimation caused by abuse of measurements. A range of simulations is performed to test the performance of the proposed method. The results confirm the effectiveness and robustness of the novel method.https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0670kalman filtersfiltering theorymonte carlo methodstarget trackingadaptive cardinality balanced multitarget multibernoulli filtercardinality overestimationparticles degradation problempoor-quality particlescubature kalman filterpredicted particletarget numberstandard smc-cbmember filter suffersmultitarget tracking problemsequential monte carlo cardinality balanced multibernoulli filter |
| spellingShingle | Haihuan Wang Xiaoyong Lyu Long Ma Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman The Journal of Engineering kalman filters filtering theory monte carlo methods target tracking adaptive cardinality balanced multitarget multibernoulli filter cardinality overestimation particles degradation problem poor-quality particles cubature kalman filter predicted particle target number standard smc-cbmember filter suffers multitarget tracking problem sequential monte carlo cardinality balanced multibernoulli filter |
| title | Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman |
| title_full | Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman |
| title_fullStr | Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman |
| title_full_unstemmed | Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman |
| title_short | Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman |
| title_sort | adaptive cardinality balanced multi target multi bernoulli filter based on cubature kalman |
| topic | kalman filters filtering theory monte carlo methods target tracking adaptive cardinality balanced multitarget multibernoulli filter cardinality overestimation particles degradation problem poor-quality particles cubature kalman filter predicted particle target number standard smc-cbmember filter suffers multitarget tracking problem sequential monte carlo cardinality balanced multibernoulli filter |
| url | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0670 |
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