{"title":"列车定位器使用惯性传感器和里程表","authors":"Petr Ernest, Roman Maz, Libor Pfeueil","doi":"10.1109/IVS.2004.1336497","DOIUrl":null,"url":null,"abstract":"The paper describes a solution to railway vehicle localization problem for the cases, where no global positioning information (like GPS) is temporarily unavailable. The given solution also assumes no additional landmarks or other extraordinary installations aside the train track. The presented approach is based on smart fusion of onboard-gathered data making use of Kalman filter. The available data sources include a vehicle odometer and accelerometer.","PeriodicalId":296386,"journal":{"name":"IEEE Intelligent Vehicles Symposium, 2004","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":"{\"title\":\"Train locator using inertial sensors and odometer\",\"authors\":\"Petr Ernest, Roman Maz, Libor Pfeueil\",\"doi\":\"10.1109/IVS.2004.1336497\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes a solution to railway vehicle localization problem for the cases, where no global positioning information (like GPS) is temporarily unavailable. The given solution also assumes no additional landmarks or other extraordinary installations aside the train track. The presented approach is based on smart fusion of onboard-gathered data making use of Kalman filter. The available data sources include a vehicle odometer and accelerometer.\",\"PeriodicalId\":296386,\"journal\":{\"name\":\"IEEE Intelligent Vehicles Symposium, 2004\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"37\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Intelligent Vehicles Symposium, 2004\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2004.1336497\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Intelligent Vehicles Symposium, 2004","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2004.1336497","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper describes a solution to railway vehicle localization problem for the cases, where no global positioning information (like GPS) is temporarily unavailable. The given solution also assumes no additional landmarks or other extraordinary installations aside the train track. The presented approach is based on smart fusion of onboard-gathered data making use of Kalman filter. The available data sources include a vehicle odometer and accelerometer.