{"title":"多目标跟踪的扩展统一方法","authors":"E. Emre, J. Seo","doi":"10.1109/CDC.1988.194479","DOIUrl":null,"url":null,"abstract":"By means of global modeling of multitarget tracking (MTT), data-association and maneuver-estimation problems can be simultaneously solved using system identification techniques. With this approach, previously developed single-target tracking-acceleration estimation techniques can also be directly used for the MTT problem. In particular multiple-model (adaptive) Kalman filtering (MMKF) can be used to obtain the optimal solution. For computational considerations, one can apply some suboptimal solutions of MMKF such as one-step conditional maximum likelihood or maximum posteriori estimation.<<ETX>>","PeriodicalId":113534,"journal":{"name":"Proceedings of the 27th IEEE Conference on Decision and Control","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An extended unifying approach to multi-target tracking\",\"authors\":\"E. Emre, J. Seo\",\"doi\":\"10.1109/CDC.1988.194479\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By means of global modeling of multitarget tracking (MTT), data-association and maneuver-estimation problems can be simultaneously solved using system identification techniques. With this approach, previously developed single-target tracking-acceleration estimation techniques can also be directly used for the MTT problem. In particular multiple-model (adaptive) Kalman filtering (MMKF) can be used to obtain the optimal solution. For computational considerations, one can apply some suboptimal solutions of MMKF such as one-step conditional maximum likelihood or maximum posteriori estimation.<<ETX>>\",\"PeriodicalId\":113534,\"journal\":{\"name\":\"Proceedings of the 27th IEEE Conference on Decision and Control\",\"volume\":\"80 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 27th IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1988.194479\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1988.194479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An extended unifying approach to multi-target tracking
By means of global modeling of multitarget tracking (MTT), data-association and maneuver-estimation problems can be simultaneously solved using system identification techniques. With this approach, previously developed single-target tracking-acceleration estimation techniques can also be directly used for the MTT problem. In particular multiple-model (adaptive) Kalman filtering (MMKF) can be used to obtain the optimal solution. For computational considerations, one can apply some suboptimal solutions of MMKF such as one-step conditional maximum likelihood or maximum posteriori estimation.<>