| 引用本文: | 苏伍各,王宏强,邓彬,等.SAR微动目标的稀疏贝叶斯成像方法.[J].国防科技大学学报,2014,36(6):128-133.[点击复制] |
| SU Wuge,WANG Hongqiang,Deng Bin,et al.The SAR micro motion target imaging via the sparse Bayesian method[J].Journal of National University of Defense Technology,2014,36(6):128-133[点击复制] |
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| SAR微动目标的稀疏贝叶斯成像方法 |
| 苏伍各1, 王宏强1, 邓彬1, 秦玉亮1, 凌永顺2 |
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(1.国防科技大学 电子科学与工程学院， 湖南 长沙 410073;2.电子工程学院，安徽 合肥 230037)
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| 摘要: |
| SAR微动信息能够反映出目标的属性信息，其微动图像可作为雷达目标识别的一种重要手段。基于SAR微动目标回波的稀疏特性，建立了在过完备词典下的稀疏表示模型，提出一种新的稀疏贝叶斯重构方法——方差成分扩张压缩，该方法仅赋予有重要意义的信号元素不同的方差分量，拥有更少的参数。仿真结果表明，方差成分扩张压缩方法能较精确地估计出SAR目标微动参数，同时能够获得低信噪比条件下较好的微动目标像。 |
| 关键词: SAR 微动目标成像 参数估计 稀疏表示 方差成分扩张压缩 |
| DOI：10.11887/j.cn.201406023 |
| 投稿日期：2014-03-14 |
| 基金项目:国家自然科学基金资助项目（61171133）；国家自然科学基金青年基金资助项目（61302148，61101182） |
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| The SAR micro motion target imaging via the sparse Bayesian method |
| SU Wuge1, WANG Hongqiang1, Deng Bin1, QIN Yuliang1, LING Yongshun2 |
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(1.College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;2. Electronic Engineering Institute, Hefei 230037, China)
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| Abstract: |
| The micro motion target image of SAR can be used in radar target recognition since it can reflect the attribute of target information. Based on the fact that the SAR echo of the micro motion target is sparse, the sparse signal representation was established under an over-complete dictionary. A new sparse Bayesian learning named expansion-compression variance-component based method was employed, which only assigns the distinct variance components to the significant signal elements. In addition, the expansion-compression variance-component based method has much less parameters. The imaging results of SAR micro motion target can estimate the micro motion parameter better, and achieve the fine SAR micro motion image under the low signal to noise ratio. |
| Keywords: synthetic aperture radar micro motion target imaging parameter estimation sparse representation ExCoV |
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