{"title":"一种单平台多传感器地面目标跟踪算法","authors":"Mengyao Wu, Yong-Ting Wang","doi":"10.1109/GNCC42960.2018.9018668","DOIUrl":null,"url":null,"abstract":"For improving the platform tracking performance, the ground target tracking fusion based on platform-level multi-source heterogeneous sensors was studied. The association of the target’s state information was built using gray algorithms, and then the results were further tested by the recognition confidence of target type using evidence theory. Additionally, with assistance of road information, the ground target tracking model sets and filter are designed for the tracking. The effectiveness of the algorithm was verified by simulation.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"72 2","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Single-Platform Multi-Sensor Ground Target Tracking Algorithm\",\"authors\":\"Mengyao Wu, Yong-Ting Wang\",\"doi\":\"10.1109/GNCC42960.2018.9018668\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For improving the platform tracking performance, the ground target tracking fusion based on platform-level multi-source heterogeneous sensors was studied. The association of the target’s state information was built using gray algorithms, and then the results were further tested by the recognition confidence of target type using evidence theory. Additionally, with assistance of road information, the ground target tracking model sets and filter are designed for the tracking. The effectiveness of the algorithm was verified by simulation.\",\"PeriodicalId\":6623,\"journal\":{\"name\":\"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)\",\"volume\":\"72 2\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GNCC42960.2018.9018668\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GNCC42960.2018.9018668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Single-Platform Multi-Sensor Ground Target Tracking Algorithm
For improving the platform tracking performance, the ground target tracking fusion based on platform-level multi-source heterogeneous sensors was studied. The association of the target’s state information was built using gray algorithms, and then the results were further tested by the recognition confidence of target type using evidence theory. Additionally, with assistance of road information, the ground target tracking model sets and filter are designed for the tracking. The effectiveness of the algorithm was verified by simulation.
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