{"title":"雷达数据的空间、时间和光谱方面","authors":"M. Cheney, Ling Wang, B. Borden","doi":"10.1109/RADAR.2009.4976931","DOIUrl":null,"url":null,"abstract":"We develop a linearized imaging theory that combines the spatial, temporal, and spectral aspects of scattered waves. We consider the case of fixed, distributed transmitters and receivers, and a general distribution of objects, each undergoing linear motion; thus the theory deals with imaging distributions in phase space. We derive a model for the data that is appropriate for any set of waveforms, and show how it specializes to familiar results when the targets are far from the antennas and when narrowband waveforms are used.","PeriodicalId":346898,"journal":{"name":"2009 IEEE Radar Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Spatial, temporal, and spectral aspects of radar data\",\"authors\":\"M. Cheney, Ling Wang, B. Borden\",\"doi\":\"10.1109/RADAR.2009.4976931\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a linearized imaging theory that combines the spatial, temporal, and spectral aspects of scattered waves. We consider the case of fixed, distributed transmitters and receivers, and a general distribution of objects, each undergoing linear motion; thus the theory deals with imaging distributions in phase space. We derive a model for the data that is appropriate for any set of waveforms, and show how it specializes to familiar results when the targets are far from the antennas and when narrowband waveforms are used.\",\"PeriodicalId\":346898,\"journal\":{\"name\":\"2009 IEEE Radar Conference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Radar Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RADAR.2009.4976931\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Radar Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR.2009.4976931","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spatial, temporal, and spectral aspects of radar data
We develop a linearized imaging theory that combines the spatial, temporal, and spectral aspects of scattered waves. We consider the case of fixed, distributed transmitters and receivers, and a general distribution of objects, each undergoing linear motion; thus the theory deals with imaging distributions in phase space. We derive a model for the data that is appropriate for any set of waveforms, and show how it specializes to familiar results when the targets are far from the antennas and when narrowband waveforms are used.