基于稀疏贝叶斯学习的扩展目标雷达关联成像*

雷达关联成像不依赖于雷达与目标的相对运动,是一种高分辨凝视成像方式。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。该算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。该算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。

Radar coincidence imaging for extended targets based on sparse Bayesian learning

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of relative motion between target and radar. Conventional RCI 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 (APC-SBL) algorithm is proposed. To model the extended target, a pattern-coupled hierarchical Gaussian prior model is introduced in SBL framework, and then the algorithm alternates between steps of target reconstruction and parameter optimization under the variational Bayesian expectation-maximization (VBEM) framework. Therefore, the reconstruction of each coefficient involves its immediate neighbors, and the parameter indicating the pattern relevance between the coefficient and its immediate neighbors is updated adaptively during the iterations. Experimental results demonstrate that the proposed algorithm could realize high resolution imaging effectively for extended target.