{"title":"基于多目标同步运动模型的雷达与二次传感器数据融合","authors":"B. Karlsen, E. Nielsen, Morten T. Pedersen","doi":"10.1109/SSPD.2015.7288504","DOIUrl":null,"url":null,"abstract":"We present a method for fusion of radar and secondary sensor data, e.g. AIS (Automatic Identification System), ADS-B (Automatic Dependent Surveillance Broadcast) or IFF (Identification, Friend or Foe) data. The method is based on fusion of kinematic models of target trajectories from the two sensors into kinematic models of the associations. The method can handle several hundred simultaneous targets (shown for 529 x 529 targets + 1600 clutter plots). It does not require several iterations through the data set in order to find associations, and it includes track history from the two sensors. The mathematical framework of the method is based on Kalman filters, maximum likelihood and probability theory as well as kinematics.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fusion of Radar and Secondary Sensor Data Using Kinematic Models of Multiple Simultaneous Targets\",\"authors\":\"B. Karlsen, E. Nielsen, Morten T. Pedersen\",\"doi\":\"10.1109/SSPD.2015.7288504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method for fusion of radar and secondary sensor data, e.g. AIS (Automatic Identification System), ADS-B (Automatic Dependent Surveillance Broadcast) or IFF (Identification, Friend or Foe) data. The method is based on fusion of kinematic models of target trajectories from the two sensors into kinematic models of the associations. The method can handle several hundred simultaneous targets (shown for 529 x 529 targets + 1600 clutter plots). It does not require several iterations through the data set in order to find associations, and it includes track history from the two sensors. The mathematical framework of the method is based on Kalman filters, maximum likelihood and probability theory as well as kinematics.\",\"PeriodicalId\":212668,\"journal\":{\"name\":\"2015 Sensor Signal Processing for Defence (SSPD)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Sensor Signal Processing for Defence (SSPD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSPD.2015.7288504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Sensor Signal Processing for Defence (SSPD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSPD.2015.7288504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
我们提出了一种融合雷达和辅助传感器数据的方法，例如AIS(自动识别系统)、ADS-B(自动相关监视广播)或IFF(敌我识别)数据。该方法基于将两个传感器的目标轨迹的运动学模型融合为关联的运动学模型。该方法可以同时处理数百个目标(如图所示为529 x 529目标+ 1600杂波图)。它不需要通过数据集进行多次迭代来找到关联，并且它包括来自两个传感器的跟踪历史。该方法的数学框架是基于卡尔曼滤波、极大似然和概率论以及运动学。
Fusion of Radar and Secondary Sensor Data Using Kinematic Models of Multiple Simultaneous Targets
We present a method for fusion of radar and secondary sensor data, e.g. AIS (Automatic Identification System), ADS-B (Automatic Dependent Surveillance Broadcast) or IFF (Identification, Friend or Foe) data. The method is based on fusion of kinematic models of target trajectories from the two sensors into kinematic models of the associations. The method can handle several hundred simultaneous targets (shown for 529 x 529 targets + 1600 clutter plots). It does not require several iterations through the data set in order to find associations, and it includes track history from the two sensors. The mathematical framework of the method is based on Kalman filters, maximum likelihood and probability theory as well as kinematics.
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