{"title":"利用单孔径合成孔径雷达对运动物体进行三维成像","authors":"M. Stuff, M. Biancalana, G. Arnold, J. Garbarino","doi":"10.1109/NRC.2004.1316402","DOIUrl":null,"url":null,"abstract":"General Dynamics Advanced Information Systems (GDAIS), supported by the USA Air Force, has been investigating exploiting moving targets whose returns are captured by conventional SAR systems. The result is a processing system that can extract the detailed 3D motions of a moving object. This system is called Three-Dimensional Motion and Geometric Information (3DMAGI). This paper reports on work done with a full volume of data from the National Ground Intelligence Center (NGIC) and vehicle trajectories measured by an inertial system on a moving vehicle. Its goal is to determine how to best use the rich data available from advanced processing to produce images and image products that will simplify the task of exploiting the radar image. The data and sample trajectory are described as well as how they are used to emulate the result of 3DMAGI processing. The work consists of investigations into the methods of creating a 3D data volume that matches the NGIC chamber collection, starting from a small subset defined by the data surface which lies in the full volume. How much extrapolation is needed to get acceptable results is the first question posed. From there, the question of just what methods yield the best results is examined. Limitations of various methods are explained with examples. Comparisons of each method of extrapolation to the original data volume are presented to give an indication of progress toward the goal.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":"{\"title\":\"Imaging moving objects in 3D from single aperture synthetic aperture radar\",\"authors\":\"M. Stuff, M. Biancalana, G. Arnold, J. Garbarino\",\"doi\":\"10.1109/NRC.2004.1316402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"General Dynamics Advanced Information Systems (GDAIS), supported by the USA Air Force, has been investigating exploiting moving targets whose returns are captured by conventional SAR systems. The result is a processing system that can extract the detailed 3D motions of a moving object. This system is called Three-Dimensional Motion and Geometric Information (3DMAGI). This paper reports on work done with a full volume of data from the National Ground Intelligence Center (NGIC) and vehicle trajectories measured by an inertial system on a moving vehicle. Its goal is to determine how to best use the rich data available from advanced processing to produce images and image products that will simplify the task of exploiting the radar image. The data and sample trajectory are described as well as how they are used to emulate the result of 3DMAGI processing. The work consists of investigations into the methods of creating a 3D data volume that matches the NGIC chamber collection, starting from a small subset defined by the data surface which lies in the full volume. How much extrapolation is needed to get acceptable results is the first question posed. From there, the question of just what methods yield the best results is examined. Limitations of various methods are explained with examples. Comparisons of each method of extrapolation to the original data volume are presented to give an indication of progress toward the goal.\",\"PeriodicalId\":268965,\"journal\":{\"name\":\"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"45\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NRC.2004.1316402\",\"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 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NRC.2004.1316402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Imaging moving objects in 3D from single aperture synthetic aperture radar
General Dynamics Advanced Information Systems (GDAIS), supported by the USA Air Force, has been investigating exploiting moving targets whose returns are captured by conventional SAR systems. The result is a processing system that can extract the detailed 3D motions of a moving object. This system is called Three-Dimensional Motion and Geometric Information (3DMAGI). This paper reports on work done with a full volume of data from the National Ground Intelligence Center (NGIC) and vehicle trajectories measured by an inertial system on a moving vehicle. Its goal is to determine how to best use the rich data available from advanced processing to produce images and image products that will simplify the task of exploiting the radar image. The data and sample trajectory are described as well as how they are used to emulate the result of 3DMAGI processing. The work consists of investigations into the methods of creating a 3D data volume that matches the NGIC chamber collection, starting from a small subset defined by the data surface which lies in the full volume. How much extrapolation is needed to get acceptable results is the first question posed. From there, the question of just what methods yield the best results is examined. Limitations of various methods are explained with examples. Comparisons of each method of extrapolation to the original data volume are presented to give an indication of progress toward the goal.