{"title":"在高速公路上使用无线通信进行定位的自举滤波","authors":"Sangwoo Cho, J. Chun","doi":"10.1109/CDC.2000.912908","DOIUrl":null,"url":null,"abstract":"We propose a new position location algorithm based on the bootstrap filtering using the time difference of arrival measurements. The proposed algorithm imposes nonlinear kinematic constraints on the state estimates without destabilizing the algorithm. Such constraints can be most naturally incorporated in the Bayesian bootstrap filtering framework. The proposed algorithm is verified through simulation, and the result demonstrates that our algorithm is more robust than the extended Kalman filter and the bootstrap filter without constraints.","PeriodicalId":217237,"journal":{"name":"Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bootstrap filtering for the position location using wireless communication on highways\",\"authors\":\"Sangwoo Cho, J. Chun\",\"doi\":\"10.1109/CDC.2000.912908\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a new position location algorithm based on the bootstrap filtering using the time difference of arrival measurements. The proposed algorithm imposes nonlinear kinematic constraints on the state estimates without destabilizing the algorithm. Such constraints can be most naturally incorporated in the Bayesian bootstrap filtering framework. The proposed algorithm is verified through simulation, and the result demonstrates that our algorithm is more robust than the extended Kalman filter and the bootstrap filter without constraints.\",\"PeriodicalId\":217237,\"journal\":{\"name\":\"Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.2000.912908\",\"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 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.2000.912908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bootstrap filtering for the position location using wireless communication on highways
We propose a new position location algorithm based on the bootstrap filtering using the time difference of arrival measurements. The proposed algorithm imposes nonlinear kinematic constraints on the state estimates without destabilizing the algorithm. Such constraints can be most naturally incorporated in the Bayesian bootstrap filtering framework. The proposed algorithm is verified through simulation, and the result demonstrates that our algorithm is more robust than the extended Kalman filter and the bootstrap filter without constraints.