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Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning

机译：基于变分稀疏贝叶斯学习的离网雷达重合成像

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摘要

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique motivated by classical optical coincidence imaging. In RCI, sparse reconstruction methods are commonly used to achieve better imaging result, while the performance guarantee is based on the general assumption that the scatterers are located at the prediscretized grid-cell centers. However, the widely existing off-grid problem degrades the RCI performance considerably. In this paper, an algorithm based on variational sparse Bayesian learning (VSBL) is developed to solve the off-grid RCI. Applying Taylor expansion, the unknown true dictionary is approximated accurately to a linear model. Then target reconstruction is reformulated as a joint sparse recovery problem that recovers three groups of sparse coefficients over three known dictionaries with the constraint of the common support shared by the groups. VSBL is then applied to solve the problem by assigning appropriate priors to the three groups of coefficients. Results of numerical experiments demonstrate that the algorithm can achieve outstanding reconstruction performance and yield superior performance both in suppressing noise and in adapting to off-grid error.
机译：雷达符合成像（RCI）是一种由经典光学符合成像驱动的高分辨率凝视成像技术。在RCI中，稀疏重建方法通常用于获得更好的成像效果，而性能保证是基于散射体位于预离散网格单元中心的一般假设。但是，广泛存在的离网问题大大降低了RCI性能。本文提出了一种基于变分稀疏贝叶斯学习（VSBL）的算法来解决离网RCI问题。应用泰勒展开式，可以将未知的真实字典准确地近似为线性模型。然后，将目标重构重新表述为联合稀疏恢复问题，该问题将在三个已知字典上恢复三组稀疏系数，并且要限制各组共享的共同支持。然后，通过为三组系数分配适当的先验，使用VSBL解决问题。数值实验结果表明，该算法在抑制噪声和适应离网误差方面均具有良好的重构性能，并具有较高的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第5期|1782178.1-1782178.12|共12页
  • 作者

    Zhou Xiaoli; Wang Hongqiang; Cheng Yongqiang; Qin Yuliang;

  • 作者单位

    Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China;

    Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China;

    Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China;

    Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China;

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