| 引用本文: | 周小利,王宏强,程永强,等.稀疏贝叶斯学习框架下的扩展目标雷达关联成像.[J].国防科技大学学报,2017,39(3):151-157.[点击复制] |
| ZHOU Xiaoli,WANG Hongqiang,CHENG Yongqiang,et al.Radar coincidence imaging for extended targets in sparse Bayesian learning framework[J].Journal of National University of Defense Technology,2017,39(3):151-157[点击复制] |
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| 稀疏贝叶斯学习框架下的扩展目标雷达关联成像 |
| 周小利, 王宏强, 程永强, 秦玉亮 |
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(国防科技大学 电子科学与工程学院， 湖南 长沙 410073)
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| 摘要: |
| 传统的关联成像方法未考虑复杂扩展目标的结构信息，在高分辨成像时的应用受到限制，为此提出一种自适应结构配对稀疏贝叶斯学习方法。该方法在稀疏贝叶斯学习的框架内针对扩展目标建立一种结构配对层次化高斯先验模型，然后采用变分贝叶斯期望－最大化算法交替进行目标重构和参数优化。该方法将某一信号分量的重构与周围信号分量联系起来，并能在迭代过程中自适应地调整表征各信号分量相关性的参数。实验结果表明，该方法针对扩展目标可以有效地进行高分辨成像。 |
| 关键词: 雷达关联成像 扩展目标 稀疏贝叶斯学习 结构配对 变分贝叶斯期望－最大化 |
| DOI：10.11887/j.cn.201703023 |
| 投稿日期：2016-01-21 |
| 基金项目:国家自然科学基金资助项目（61302149, 61302142）；高等学校博士学科点专项科研基金博导类资助项目（20124307110013） |
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| Radar coincidence imaging for extended targets in sparse Bayesian learning framework |
| ZHOU Xiaoli, WANG Hongqiang, CHENG Yongqiang, QIN Yuliang |
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(College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Radar coincidence imaging is a high-resolution staring imaging technique without the limitation of relative motion between target and radar. Conventional radar coincidence imaging methods ignore the structure information of complex extended target, which limits its applications in high resolution imaging, thus an adaptive pattern-coupled sparse Bayesian learning algorithm was proposed. To model the extended target, a pattern-coupled hierarchical Gaussian prior model was introduced in sparse Bayesian learning framework, and then the algorithm alternated between steps of target reconstruction and parameter optimization under the variational Bayesian expectation maximization framework. Therefore, the reconstruction of each coefficient involved its immediate neighbors, and the parameter indicating the pattern relevance between the coefficient and its immediate neighbors was updated adaptively during the iterations. Experimental results demonstrate that the proposed algorithm can achieve high resolution imaging effectively for the extended target. |
| Keywords: radar coincidence imaging extended target sparse Bayesian learning pattern-coupled variational Bayesian expectation maximization |
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