{"title":"合成孔径雷达断层扫描","authors":"Longlong Yu, Xiaotao Huang, Dong Feng, Jian Wang","doi":"10.23919/CISS51089.2021.9652324","DOIUrl":null,"url":null,"abstract":"This paper presents a synthetic aperture radar tomography (TomoSAR) technique able to reduce the number of acquisitions and, at the same time, to achieve super-resolution performance. The technique consists of a new baseline geometry and of a tailored reconstruction method. The new baseline is configured according to the coprime array geometry. Naturally, we name the proposed technique as the \"coprime TomoSAR\". The coprime acquisition mode requires fewer acquisitions than the uniform one for obtaining the same baseline aperture. To further improve tomographic resolution and to reject ambiguity problem induced by sparsely sampling of the coprime acquisition mode, we perform the tomographic reconstruction using the root-multiple signal classification (Root-MUSIC) algorithm. More important is that the Root-MUSIC algorithm can exploit the difference co-baseline in the tomographic reconstruction process. The exploition of the difference co-baseline ensures that the coprime TomoSAR provides comparable tomographic performance to the uniform TomoSAR when the two TomoSAR have the same baseline aperture length. This is validated by simulation experiments.","PeriodicalId":318218,"journal":{"name":"2021 2nd China International SAR Symposium (CISS)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Coprime Synthetic Aperture Radar Tomography\",\"authors\":\"Longlong Yu, Xiaotao Huang, Dong Feng, Jian Wang\",\"doi\":\"10.23919/CISS51089.2021.9652324\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a synthetic aperture radar tomography (TomoSAR) technique able to reduce the number of acquisitions and, at the same time, to achieve super-resolution performance. The technique consists of a new baseline geometry and of a tailored reconstruction method. The new baseline is configured according to the coprime array geometry. Naturally, we name the proposed technique as the \\\"coprime TomoSAR\\\". The coprime acquisition mode requires fewer acquisitions than the uniform one for obtaining the same baseline aperture. To further improve tomographic resolution and to reject ambiguity problem induced by sparsely sampling of the coprime acquisition mode, we perform the tomographic reconstruction using the root-multiple signal classification (Root-MUSIC) algorithm. More important is that the Root-MUSIC algorithm can exploit the difference co-baseline in the tomographic reconstruction process. The exploition of the difference co-baseline ensures that the coprime TomoSAR provides comparable tomographic performance to the uniform TomoSAR when the two TomoSAR have the same baseline aperture length. This is validated by simulation experiments.\",\"PeriodicalId\":318218,\"journal\":{\"name\":\"2021 2nd China International SAR Symposium (CISS)\",\"volume\":\"174 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd China International SAR Symposium (CISS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/CISS51089.2021.9652324\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd China International SAR Symposium (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CISS51089.2021.9652324","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a synthetic aperture radar tomography (TomoSAR) technique able to reduce the number of acquisitions and, at the same time, to achieve super-resolution performance. The technique consists of a new baseline geometry and of a tailored reconstruction method. The new baseline is configured according to the coprime array geometry. Naturally, we name the proposed technique as the "coprime TomoSAR". The coprime acquisition mode requires fewer acquisitions than the uniform one for obtaining the same baseline aperture. To further improve tomographic resolution and to reject ambiguity problem induced by sparsely sampling of the coprime acquisition mode, we perform the tomographic reconstruction using the root-multiple signal classification (Root-MUSIC) algorithm. More important is that the Root-MUSIC algorithm can exploit the difference co-baseline in the tomographic reconstruction process. The exploition of the difference co-baseline ensures that the coprime TomoSAR provides comparable tomographic performance to the uniform TomoSAR when the two TomoSAR have the same baseline aperture length. This is validated by simulation experiments.