| 题名: | A SPARSE OPTIMIZATION METHOD FOR MASKING EFFECT REMOVAL IN NOISE SYNTHETIC APERTURE RADAR |
| 作者: | Dong, Xiao; Yunhua Zhang(张云华) ; Zhai, Wenshuai
|
| 作者部门: | 微波遥感部
|
| 通讯作者: | Dong, X (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Microwave Remote Sensing, Beijing 100190, Peoples R China.
|
| 会议名称: | 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
|
| 会议日期: | JUL 10-15, 2016
|
| 会议地点: | Beijing, PEOPLES R CHINA
|
| 会议录: | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
|
| 出版日期: | 2016
|
| 收录类别: | EI
; CPCI
|
| 页码: | 6811-6814
|
| ISSN号: | 2153-6996
|
| ISBN号: | 978-1-5090-3332-4
|
| 关键词: | Compressed sensing
; inverse problem
; masking effect
; noise radar
; sparse optimization
; synthetic aperture radar (SAR)
|
| 英文摘要: | In this paper, we propose a sparse optimization method to suppress the masking effect of noise synthetic aperture radar (SAR) imaging. The transmitted noise waveforms have large fluctuations in the spectrum, which results in high-level sidelobes in the range direction. Weak targets can be masked by the sidelobes of strong targets when traditional matched filtering (MF) is applied. Inspired by the idea of compressed sensing (CS), we formulate the noise SAR imaging process as solving an inverse problem from incomplete random frequency measurements, and then reconstruct the noise SAR image using sparse optimization method. Experimental results on real airborne noise SAR data are presented to show the effectiveness of the proposed method. |
| 语种: | 英语
|
| 内容类型: | 会议论文
|
| URI标识: | http://ir.nssc.ac.cn/handle/122/5759
|
| Appears in Collections: | 微波遥感部_会议论文
|
| File Name/ File Size |
Content Type |
Version |
Access |
License |
|
|---|
| 201607730778.pdf（513KB） | 会议论文 | -- | 限制开放 | | View
联系获取全文 |
|
| Recommended Citation: | |
Dong, Xiao,Zhang, Yunhua,Zhai, Wenshuai. A SPARSE OPTIMIZATION METHOD FOR MASKING EFFECT REMOVAL IN NOISE SYNTHETIC APERTURE RADAR[C]. 见:36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, PEOPLES R CHINA. JUL 10-15, 2016.
|
|
|
