{"title":"变换域分解聚光灯模式sar原始数据的自回归建模","authors":"T. Ikuma, M. Naraghi-Pour, T. Lewis","doi":"10.1109/IGARSS.2010.5653086","DOIUrl":null,"url":null,"abstract":"Raw data collected by synthetic aperture radar (SAR) is commonly assumed to be uncorrelated and with a zero-mean Gaussian distribution. In this paper, we show—both analytically and numerically—that the range-wise inverse Fourier transform of the dechirp-on-receive circular SAR data exhibits significant correlation in the azimuth direction. Moreover, we show that a block adaptive autoregressive model well represents the transformed SAR data.","PeriodicalId":406785,"journal":{"name":"2010 IEEE International Geoscience and Remote Sensing Symposium","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Autoregressive modeling of dechirped spotlight-mode sar rawdata in transform domain\",\"authors\":\"T. Ikuma, M. Naraghi-Pour, T. Lewis\",\"doi\":\"10.1109/IGARSS.2010.5653086\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Raw data collected by synthetic aperture radar (SAR) is commonly assumed to be uncorrelated and with a zero-mean Gaussian distribution. In this paper, we show—both analytically and numerically—that the range-wise inverse Fourier transform of the dechirp-on-receive circular SAR data exhibits significant correlation in the azimuth direction. Moreover, we show that a block adaptive autoregressive model well represents the transformed SAR data.\",\"PeriodicalId\":406785,\"journal\":{\"name\":\"2010 IEEE International Geoscience and Remote Sensing Symposium\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Geoscience and Remote Sensing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2010.5653086\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2010.5653086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Autoregressive modeling of dechirped spotlight-mode sar rawdata in transform domain
Raw data collected by synthetic aperture radar (SAR) is commonly assumed to be uncorrelated and with a zero-mean Gaussian distribution. In this paper, we show—both analytically and numerically—that the range-wise inverse Fourier transform of the dechirp-on-receive circular SAR data exhibits significant correlation in the azimuth direction. Moreover, we show that a block adaptive autoregressive model well represents the transformed SAR data.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
