教授 博士生导师
招生学科专业:
信息与通信工程 -- 【招收博士、硕士研究生】 -- 电子信息工程学院
探测与成像 -- 【招收硕士研究生】 -- 电子信息工程学院
电子信息 -- 【招收博士、硕士研究生】 -- 电子信息工程学院
毕业院校:南京航空航天大学
学历:南京航空航天大学
学位:工学博士学位
所在单位:电子信息工程学院
办公地点:南京市江宁区将军大道29号
电子信息工程学院楼122
邮编:211106
联系方式:电话 (025)84896490-4122 传真 86-25-84892452 邮件 tulip_wling@nuaa.edu.cn (国内) wanglrpizess@163.com wanglrpi@gmail.com (国外)
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所属单位:电子信息工程学院
发表刊物:SIGNAL PROCESSING
关键字:Compressive sensing (CS) Inverse synthetic aperture radar (ISAR) Kalman Filter Greedy algorithm Sparse recovery
摘要:The Compressive sensing (CS) theory provides a novel type of image reconstruction methods for radar imaging. A good image can be obtained using much less data as compared to the conventional imaging methods, however under the sparse assumptions of the scene/target in certain domains. In this paper, we present a Kalman filter based sparse reconstruction approach for ISAR imaging. As the Kalman filter has robust and excellent estimation performance in statistical settings for linear problems, it leads to good image reconstruction results for real ISAR data. In addition to the spatial sparsity of the scene, we exploit the sparsity in wavelet domain to improve the reconstruction of region-like features in the target image other than point-like features. The images obtained by assuming the sparsity in different domains are synthesized to further improve the image reconstruction. The ISAR real data processing demonstrates the performance of the Kalman filter based sparse ISAR imaging method and the effectiveness of the image synthesis methods. (C) 2017 Elsevier B.V. All rights reserved.
ISSN号:0165-1684
是否译文:否
发表时间:2017-09-01
合写作者:Loffeld, Otmar,Ma, Kaili,Qian, Yulei
通讯作者:汪玲