基于方差成分扩张压缩的稀疏贝叶斯ISAR成像方法

发布时间：2018-08-02 13:30

【摘要】：基于贝叶斯框架下的稀疏重构方法,由于考虑了稀疏信号的先验信息以及测量过程中的加性噪声,因而能够更好地重建目标系数,然而传统的稀疏贝叶斯学习(SBL)算法参数多,时效性差。该文考虑一种新的稀疏贝叶斯学习方法方差成分扩张压缩(ExCoV),其不同于SBL中赋予所有的信号元素各自的方差分量参数,ExCoV方法仅仅赋予有重要意义的信号元素不同的方差分量,并拥有比SBL方法更少的参数。基于计算机层析成像技术框架下的ISAR成像模型,该文将ExCoV方法结合压缩感知(CS)理论将其进行ISAR成像,并从适用性和成像效果等方面与常用的极坐标格式算法(PFA),卷积逆投影算法(CBPA)和传统的稀疏重构算法进行比较,点目标仿真结果表明基于ExCoV的方法得到的ISAR像具有低旁瓣,高分辨率的特点,真实数据的成像结果表明该方法是一种比SBL更有效的ISAR成像算法。
[Abstract]:The sparse reconstruction method based on Bayesian framework takes into account the prior information of the sparse signal and the additive noise in the measurement process, so it can better reconstruct the target coefficients. However, the traditional sparse Bayesian learning (SBL) algorithm has many parameters. Poor timeliness. In this paper, we consider a new sparse Bayesian learning method called Variance component expansion Compression (ExCoV), which is different from the variance component parameter given to all signal elements in SBL. And has fewer parameters than the SBL method. Based on the ISAR imaging model under the framework of computer tomography technology, the ExCoV method is combined with the compression sensing (CS) theory to image ISAR. Compared with (PFA), convolution inverse projection algorithm and traditional sparse reconstruction algorithm, the point target simulation results show that the ISAR image based on ExCoV method has low sidelobe. Because of the high resolution, the real data imaging results show that this method is a more effective ISAR imaging algorithm than SBL.
【作者单位】：国防科技大学空间电子信息技术研究所;电子工程学院;
【基金】：国家自然科学基金(61171133) 国家自然科学青年基金(61101182) 湖南省杰出青年基金(11JJ1010)资助课题
【分类号】：TN957.52

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