{"title":"跟踪中的保证区域","authors":"D. Sworder, J. E. Boyd, R. Hutchins","doi":"10.1117/12.775820","DOIUrl":null,"url":null,"abstract":"An assurance region at level p, AP=p, is an area in motion space that contains the target with assigned probability p. It is on the basis of AP=p that an action is taken or a decision made. Common model-based trackers generate a synthetic distribution function for the kinematic state of the target. Unfortunately, this distribution is very coarse, and the resulting AP=p lack credibility. It is shown that a map-enhanced, multiple model algorithm reduces the tracking error and leads to a compact assurance region.","PeriodicalId":133868,"journal":{"name":"SPIE Defense + Commercial Sensing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assurance regions in tracking\",\"authors\":\"D. Sworder, J. E. Boyd, R. Hutchins\",\"doi\":\"10.1117/12.775820\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An assurance region at level p, AP=p, is an area in motion space that contains the target with assigned probability p. It is on the basis of AP=p that an action is taken or a decision made. Common model-based trackers generate a synthetic distribution function for the kinematic state of the target. Unfortunately, this distribution is very coarse, and the resulting AP=p lack credibility. It is shown that a map-enhanced, multiple model algorithm reduces the tracking error and leads to a compact assurance region.\",\"PeriodicalId\":133868,\"journal\":{\"name\":\"SPIE Defense + Commercial Sensing\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SPIE Defense + Commercial Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.775820\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SPIE Defense + Commercial Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.775820","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An assurance region at level p, AP=p, is an area in motion space that contains the target with assigned probability p. It is on the basis of AP=p that an action is taken or a decision made. Common model-based trackers generate a synthetic distribution function for the kinematic state of the target. Unfortunately, this distribution is very coarse, and the resulting AP=p lack credibility. It is shown that a map-enhanced, multiple model algorithm reduces the tracking error and leads to a compact assurance region.