{"title":"广义标记多伯努利滤波器的测量驱动出生模型","authors":"Shoufeng Lin, B. Vo, S. Nordholm","doi":"10.1109/ICCAIS.2016.7822442","DOIUrl":null,"url":null,"abstract":"This paper presents a measurement driven birth (MDB) model for the generalized labeled multi-Bernoulli (GLMB) filter. The MDB model adaptively generates target births based on measurement data, thereby eliminating the dependence of a priori knowledge of target birth distributions. Numerical results are provided to demonstrate the performance.","PeriodicalId":407031,"journal":{"name":"2016 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"329 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Measurement driven birth model for the generalized labeled multi-Bernoulli filter\",\"authors\":\"Shoufeng Lin, B. Vo, S. Nordholm\",\"doi\":\"10.1109/ICCAIS.2016.7822442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a measurement driven birth (MDB) model for the generalized labeled multi-Bernoulli (GLMB) filter. The MDB model adaptively generates target births based on measurement data, thereby eliminating the dependence of a priori knowledge of target birth distributions. Numerical results are provided to demonstrate the performance.\",\"PeriodicalId\":407031,\"journal\":{\"name\":\"2016 International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"volume\":\"329 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAIS.2016.7822442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Control, Automation and Information Sciences (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAIS.2016.7822442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Measurement driven birth model for the generalized labeled multi-Bernoulli filter
This paper presents a measurement driven birth (MDB) model for the generalized labeled multi-Bernoulli (GLMB) filter. The MDB model adaptively generates target births based on measurement data, thereby eliminating the dependence of a priori knowledge of target birth distributions. Numerical results are provided to demonstrate the performance.