Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference
Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. Howev...
| Príomhchruthaitheoirí: | , , , |
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| Formáid: | Alt |
| Teanga: | English |
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MDPI AG
2020-11-01
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| Sraith: | Sensors |
| Ábhair: | |
| Rochtain ar líne: | https://www.mdpi.com/1424-8220/20/22/6487 |
| _version_ | 1827702234213777408 |
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| author | Xiaobo Chen Yanjun Wang Ling Chen Jianyu Ji |
| author_facet | Xiaobo Chen Yanjun Wang Ling Chen Jianyu Ji |
| author_sort | Xiaobo Chen |
| collection | DOAJ |
| description | Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time. |
| first_indexed | 2024-03-10T14:51:26Z |
| format | Article |
| id | doaj.art-0989df17e363445f834b2daa3b9a9fa5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:51:26Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0989df17e363445f834b2daa3b9a9fa52023-11-20T20:53:40ZengMDPI AGSensors1424-82202020-11-012022648710.3390/s20226487Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian InferenceXiaobo Chen0Yanjun Wang1Ling Chen2Jianyu Ji3Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaCooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.https://www.mdpi.com/1424-8220/20/22/6487target trackingcooperative perceptionvariational Bayesian inferencejoint state estimation |
| spellingShingle | Xiaobo Chen Yanjun Wang Ling Chen Jianyu Ji Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference Sensors target tracking cooperative perception variational Bayesian inference joint state estimation |
| title | Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference |
| title_full | Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference |
| title_fullStr | Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference |
| title_full_unstemmed | Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference |
| title_short | Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference |
| title_sort | multi vehicle cooperative target tracking with time varying localization uncertainty via recursive variational bayesian inference |
| topic | target tracking cooperative perception variational Bayesian inference joint state estimation |
| url | https://www.mdpi.com/1424-8220/20/22/6487 |
| work_keys_str_mv | AT xiaobochen multivehiclecooperativetargettrackingwithtimevaryinglocalizationuncertaintyviarecursivevariationalbayesianinference AT yanjunwang multivehiclecooperativetargettrackingwithtimevaryinglocalizationuncertaintyviarecursivevariationalbayesianinference AT lingchen multivehiclecooperativetargettrackingwithtimevaryinglocalizationuncertaintyviarecursivevariationalbayesianinference AT jianyuji multivehiclecooperativetargettrackingwithtimevaryinglocalizationuncertaintyviarecursivevariationalbayesianinference |