{"title":"卡尔曼滤波算法在陆地车辆导航中的应用","authors":"V. Jeralovičs, A. Levinskis","doi":"10.1109/BEC.2014.7320554","DOIUrl":null,"url":null,"abstract":"This paper presents an algorithm based on Kalman filtering approach that is used to estimate position and heading of a land vehicle. Source of data for the system are acquisition of low cost (Global Positioning System) GPS receiver, low cost (microelectromechanical systems) MEMS gyroscope and odometer sensor. The algorithm allows defining current position and heading of vehicle when GPS signal is unavailable. To demonstrate the estimation performance of algorithm the number of experiments was performed. As the result obtained data is described in this paper.","PeriodicalId":348260,"journal":{"name":"2014 14th Biennial Baltic Electronic Conference (BEC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The analysis of Kalman filtering algorithm for land vehicle navigation\",\"authors\":\"V. Jeralovičs, A. Levinskis\",\"doi\":\"10.1109/BEC.2014.7320554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an algorithm based on Kalman filtering approach that is used to estimate position and heading of a land vehicle. Source of data for the system are acquisition of low cost (Global Positioning System) GPS receiver, low cost (microelectromechanical systems) MEMS gyroscope and odometer sensor. The algorithm allows defining current position and heading of vehicle when GPS signal is unavailable. To demonstrate the estimation performance of algorithm the number of experiments was performed. As the result obtained data is described in this paper.\",\"PeriodicalId\":348260,\"journal\":{\"name\":\"2014 14th Biennial Baltic Electronic Conference (BEC)\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 14th Biennial Baltic Electronic Conference (BEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BEC.2014.7320554\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 14th Biennial Baltic Electronic Conference (BEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BEC.2014.7320554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The analysis of Kalman filtering algorithm for land vehicle navigation
This paper presents an algorithm based on Kalman filtering approach that is used to estimate position and heading of a land vehicle. Source of data for the system are acquisition of low cost (Global Positioning System) GPS receiver, low cost (microelectromechanical systems) MEMS gyroscope and odometer sensor. The algorithm allows defining current position and heading of vehicle when GPS signal is unavailable. To demonstrate the estimation performance of algorithm the number of experiments was performed. As the result obtained data is described in this paper.