{"title":"一个标记的随机有限集刷出模型","authors":"Daniel S. Bryant, B. Vo, B. Vo, B. Jones","doi":"10.1109/ICCAIS.2017.8217579","DOIUrl":null,"url":null,"abstract":"Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to capture the lineages of spawned target tracks. In this paper, we propose a labeled random finite set spawning model and derive the resulting multi-target prediction and filtering densities. This formulation enables the joint estimation of spawned object's state and and information regarding its lineage.","PeriodicalId":410094,"journal":{"name":"2017 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A labeled random finite set spawning model\",\"authors\":\"Daniel S. Bryant, B. Vo, B. Vo, B. Jones\",\"doi\":\"10.1109/ICCAIS.2017.8217579\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to capture the lineages of spawned target tracks. In this paper, we propose a labeled random finite set spawning model and derive the resulting multi-target prediction and filtering densities. This formulation enables the joint estimation of spawned object's state and and information regarding its lineage.\",\"PeriodicalId\":410094,\"journal\":{\"name\":\"2017 International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAIS.2017.8217579\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Control, Automation and Information Sciences (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAIS.2017.8217579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to capture the lineages of spawned target tracks. In this paper, we propose a labeled random finite set spawning model and derive the resulting multi-target prediction and filtering densities. This formulation enables the joint estimation of spawned object's state and and information regarding its lineage.