Low-complexity ATPM-VSIMM algorithm with adaptive model parameters
Aiming at the problem that for maneuvering target tracking, the accuracy of tracking degraded in interacting multiple model algorithms due to the fixed model sets and the fixed transition probability matrix, a low-complexity ATPM-VSIMM algorithm was proposed, which could update the model parameters...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023186/ |
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| _version_ | 1850098203380154368 |
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| author | Hao ZENG Wangqiang MU Yang JIANG Shunping YANG |
| author_facet | Hao ZENG Wangqiang MU Yang JIANG Shunping YANG |
| author_sort | Hao ZENG |
| collection | DOAJ |
| description | Aiming at the problem that for maneuvering target tracking, the accuracy of tracking degraded in interacting multiple model algorithms due to the fixed model sets and the fixed transition probability matrix, a low-complexity ATPM-VSIMM algorithm was proposed, which could update the model parameters adaptively.The maneuvering situation of the target was judged according to the innovation changes of the system, and the state noise of the model sets was adjusted to realize the adaptive update of the model sets.Then, the more accurate transition probability matrix was computed through the change of the model posterior probability and the inter-model switching relationship.Therefore, the matching degree between the system motion model and the target motion trajectory was improved.Finally, the high filtering accuracy and the fast response speed of the tracking system were guaranteed.The effectiveness of the proposed algorithm was verified through three aspects that are the initial value of the model posterior probability, the initial value of the transition probability matrix, and the state noise.Simulation results demonstrate that the filtering accuracy of the ATPM-VSIMM algorithm is improved about 8% compared with the existing algorithms. |
| format | Article |
| id | doaj-art-419a5f2caa044d7fb1f03eab104551c7 |
| institution | DOAJ |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-419a5f2caa044d7fb1f03eab104551c72025-08-20T02:40:47ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-09-0144253559835745Low-complexity ATPM-VSIMM algorithm with adaptive model parametersHao ZENGWangqiang MUYang JIANGShunping YANGAiming at the problem that for maneuvering target tracking, the accuracy of tracking degraded in interacting multiple model algorithms due to the fixed model sets and the fixed transition probability matrix, a low-complexity ATPM-VSIMM algorithm was proposed, which could update the model parameters adaptively.The maneuvering situation of the target was judged according to the innovation changes of the system, and the state noise of the model sets was adjusted to realize the adaptive update of the model sets.Then, the more accurate transition probability matrix was computed through the change of the model posterior probability and the inter-model switching relationship.Therefore, the matching degree between the system motion model and the target motion trajectory was improved.Finally, the high filtering accuracy and the fast response speed of the tracking system were guaranteed.The effectiveness of the proposed algorithm was verified through three aspects that are the initial value of the model posterior probability, the initial value of the transition probability matrix, and the state noise.Simulation results demonstrate that the filtering accuracy of the ATPM-VSIMM algorithm is improved about 8% compared with the existing algorithms.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023186/maneuvering target trackingadaptive state noise covariance matrixadaptive transition probability matrixvariable structure interacting multiple model |
| spellingShingle | Hao ZENG Wangqiang MU Yang JIANG Shunping YANG Low-complexity ATPM-VSIMM algorithm with adaptive model parameters Tongxin xuebao maneuvering target tracking adaptive state noise covariance matrix adaptive transition probability matrix variable structure interacting multiple model |
| title | Low-complexity ATPM-VSIMM algorithm with adaptive model parameters |
| title_full | Low-complexity ATPM-VSIMM algorithm with adaptive model parameters |
| title_fullStr | Low-complexity ATPM-VSIMM algorithm with adaptive model parameters |
| title_full_unstemmed | Low-complexity ATPM-VSIMM algorithm with adaptive model parameters |
| title_short | Low-complexity ATPM-VSIMM algorithm with adaptive model parameters |
| title_sort | low complexity atpm vsimm algorithm with adaptive model parameters |
| topic | maneuvering target tracking adaptive state noise covariance matrix adaptive transition probability matrix variable structure interacting multiple model |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023186/ |
| work_keys_str_mv | AT haozeng lowcomplexityatpmvsimmalgorithmwithadaptivemodelparameters AT wangqiangmu lowcomplexityatpmvsimmalgorithmwithadaptivemodelparameters AT yangjiang lowcomplexityatpmvsimmalgorithmwithadaptivemodelparameters AT shunpingyang lowcomplexityatpmvsimmalgorithmwithadaptivemodelparameters |