{"title":"概率近程雷达数据融合","authors":"E. Richter, Frederik Preckwinkel, G. Wanielik","doi":"10.1109/SSD.2012.6198027","DOIUrl":null,"url":null,"abstract":"This paper demonstrates a system which uses two short range radar sensors and on-board kinematic sensors in order to track vehicles driving behind the ego vehicle. For that, integrated probabilistic data association and the unscented Kalman filtering framework is utilized. The functionality of the proposed system is shown using real data and its performance is critically discussed.","PeriodicalId":425823,"journal":{"name":"International Multi-Conference on Systems, Sygnals & Devices","volume":"668 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probabilistic short range radar data fusion\",\"authors\":\"E. Richter, Frederik Preckwinkel, G. Wanielik\",\"doi\":\"10.1109/SSD.2012.6198027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper demonstrates a system which uses two short range radar sensors and on-board kinematic sensors in order to track vehicles driving behind the ego vehicle. For that, integrated probabilistic data association and the unscented Kalman filtering framework is utilized. The functionality of the proposed system is shown using real data and its performance is critically discussed.\",\"PeriodicalId\":425823,\"journal\":{\"name\":\"International Multi-Conference on Systems, Sygnals & Devices\",\"volume\":\"668 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Multi-Conference on Systems, Sygnals & Devices\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSD.2012.6198027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Multi-Conference on Systems, Sygnals & Devices","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSD.2012.6198027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper demonstrates a system which uses two short range radar sensors and on-board kinematic sensors in order to track vehicles driving behind the ego vehicle. For that, integrated probabilistic data association and the unscented Kalman filtering framework is utilized. The functionality of the proposed system is shown using real data and its performance is critically discussed.
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