{"title":"高斯混合跟踪:MHT与ITS的比较","authors":"T. Song, D. Musicki, Hyoung-Won Kim, F. Govaers","doi":"10.1109/SDF.2013.6698257","DOIUrl":null,"url":null,"abstract":"A Gaussian Mixture (GM) target tracking solution is a natural consequence of the multi-target tracking in clutter, given a linear target trajectory propagation and a linear target measurement equation. We examine and compare two prominent GM target trackers: the Multi Hypothesis Tracking (MHT) and the Integrated Track Splitting. Both incorporate the false track discrimination capabilities, enabling automatic target tracking in the presence of clutter measurements and missed detections.","PeriodicalId":228075,"journal":{"name":"2013 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Gaussian mixture tracking: MHT and ITS comparison\",\"authors\":\"T. Song, D. Musicki, Hyoung-Won Kim, F. Govaers\",\"doi\":\"10.1109/SDF.2013.6698257\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A Gaussian Mixture (GM) target tracking solution is a natural consequence of the multi-target tracking in clutter, given a linear target trajectory propagation and a linear target measurement equation. We examine and compare two prominent GM target trackers: the Multi Hypothesis Tracking (MHT) and the Integrated Track Splitting. Both incorporate the false track discrimination capabilities, enabling automatic target tracking in the presence of clutter measurements and missed detections.\",\"PeriodicalId\":228075,\"journal\":{\"name\":\"2013 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SDF.2013.6698257\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SDF.2013.6698257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Gaussian Mixture (GM) target tracking solution is a natural consequence of the multi-target tracking in clutter, given a linear target trajectory propagation and a linear target measurement equation. We examine and compare two prominent GM target trackers: the Multi Hypothesis Tracking (MHT) and the Integrated Track Splitting. Both incorporate the false track discrimination capabilities, enabling automatic target tracking in the presence of clutter measurements and missed detections.