基于杂波对消-自聚焦的多通道SAR-GMTI
收稿日期: 2014-06-24
修回日期: 2014-11-17
网络出版日期: 2014-12-04
基金资助
国家自然科学基金(61301212); 国防基础科研项目(B2520110008); 江苏省研究生培养创新工程(KYLX_0274); 中国博士后科学基金(2012M511750); 航空科学基金(20132052030); 南京航空航天大学基本科研业务费(NS2013023); 江苏高校优势学科建设工程
Multichannel SAR-GMTI based on clutter cancellation and autofocus
Received date: 2014-06-24
Revised date: 2014-11-17
Online published: 2014-12-04
Supported by
National Natural Science Foundation of China (61301212); National Defense Basic Research Program (B2520110008); Funding of Jiangsu Innovation Program for Graduate Education (KYLX_0274); China Postdoctoral Science Foundation (2012M511750); Aeronautical Science Foundation of China (20132052030); NUAA Fundamental Research Funds (NS2013023); Priority Academic Program Development of Jiangsu Higher Education Institutions
韦北余 , 朱岱寅 , 吴迪 . 基于杂波对消-自聚焦的多通道SAR-GMTI[J]. 航空学报, 2015 , 36(5) : 1585 -1595 . DOI: 10.7527/S1000-6893.2014.0315
The signal process technique of ultra-high frequency (UHF) band multichannel synthetic aperture radar (SAR) moving target detection is studied. The problem of moving target blurring caused by long coherence time in azimuth is solved. The sub-block image autofocus technique is proposed to process the clutter suppressed image of the multichannel SAR ground moving target indication (GMTI) system. The depth of the moving target focusing is increased after autofocus. The contrast between the moving target and the surrounding clutter is increased. The detecting performance of the constant false alarm ratio (CFAR) detector is improved. Compared with traditional method which is implemented by directly using CFAR detector after the clutter suppression, the detecting false alarm probability of the proposed method is lower. Processing results of the real collection data show that the signal to clutter ratio of the moving target increases significantly. Moving targets are well focused in azimuth after the processing of autofocus. The effectiveness and feasibility of the method are demonstrated by the processing results of the real collection data.
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