{"title":"机动目标跟踪的自适应平滑预测器","authors":"G. Favier, A. Smolders","doi":"10.1109/CDC.1984.272124","DOIUrl":null,"url":null,"abstract":"A new approach is presented to solving the problem of maneuvering target tracking. This approach combines maneuver detection and parameter estimation through use of an Adaptive Recursive Least Squares algorithm in U-D form, including a forgetting factor. New self-tuning smoother-predictors are proposed to predict the target trajectory. The behavior of such adaptive predictors is illustrated using real radar measurements.","PeriodicalId":269680,"journal":{"name":"The 23rd IEEE Conference on Decision and Control","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1984-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Adaptive smoother-predictors for tracking maneuvering targets\",\"authors\":\"G. Favier, A. Smolders\",\"doi\":\"10.1109/CDC.1984.272124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new approach is presented to solving the problem of maneuvering target tracking. This approach combines maneuver detection and parameter estimation through use of an Adaptive Recursive Least Squares algorithm in U-D form, including a forgetting factor. New self-tuning smoother-predictors are proposed to predict the target trajectory. The behavior of such adaptive predictors is illustrated using real radar measurements.\",\"PeriodicalId\":269680,\"journal\":{\"name\":\"The 23rd IEEE Conference on Decision and Control\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 23rd IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1984.272124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 23rd IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1984.272124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive smoother-predictors for tracking maneuvering targets
A new approach is presented to solving the problem of maneuvering target tracking. This approach combines maneuver detection and parameter estimation through use of an Adaptive Recursive Least Squares algorithm in U-D form, including a forgetting factor. New self-tuning smoother-predictors are proposed to predict the target trajectory. The behavior of such adaptive predictors is illustrated using real radar measurements.
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