{"title":"不同视场分布式传感器的广义协方差联合融合方法","authors":"Feng Ma, Huan-zhang Lu, Luping Zhang, Xinglin Sheng","doi":"10.1117/12.2604692","DOIUrl":null,"url":null,"abstract":"This paper proposes a generalized covariance union(GCU) approach to solve the distributed fusion problem of sensors with different fields of view (FoVs). It uses the fusion results within the intersection of the FoVs (IoF)to estimate the(target positioning) measurement error, and then employs this estimated error to correct the multitarget densities outside the IoF. Compared with the current approach, GCU approach is more robust to the sensor-related measurement error. Simulation experiments verified the effectiveness of the proposed approaches.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"120 9 1","pages":"119130E - 119130E-11"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The generalized covariance union fusion approach for distributed sensors with different fields of view\",\"authors\":\"Feng Ma, Huan-zhang Lu, Luping Zhang, Xinglin Sheng\",\"doi\":\"10.1117/12.2604692\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a generalized covariance union(GCU) approach to solve the distributed fusion problem of sensors with different fields of view (FoVs). It uses the fusion results within the intersection of the FoVs (IoF)to estimate the(target positioning) measurement error, and then employs this estimated error to correct the multitarget densities outside the IoF. Compared with the current approach, GCU approach is more robust to the sensor-related measurement error. Simulation experiments verified the effectiveness of the proposed approaches.\",\"PeriodicalId\":90079,\"journal\":{\"name\":\"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging\",\"volume\":\"120 9 1\",\"pages\":\"119130E - 119130E-11\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2604692\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2604692","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The generalized covariance union fusion approach for distributed sensors with different fields of view
This paper proposes a generalized covariance union(GCU) approach to solve the distributed fusion problem of sensors with different fields of view (FoVs). It uses the fusion results within the intersection of the FoVs (IoF)to estimate the(target positioning) measurement error, and then employs this estimated error to correct the multitarget densities outside the IoF. Compared with the current approach, GCU approach is more robust to the sensor-related measurement error. Simulation experiments verified the effectiveness of the proposed approaches.