基于非参数广义反射率模型的高分辨率微波成像快速算法
Fast Algorithm of High-resolution Microwave Imaging Using the Non-parametric Generalized Reflectivity Model
摘要：
提出了一种基于广义反射率概念的高分辨率微波成像算法。本文的贡献是双重的。我们引入了非参数广义反射率（GR，简称）的概念，作为工作频率和视角等的函数。GR扩展了传统的基于Born的成像模型，即，单散射模型，考虑到电磁波场和成像场景之间更真实的相互作用。然后，基于稀疏正则化优化的凸性，制定了GR的微波成像。通常，稀疏正则化优化需要实现迭代策略，这在计算上是昂贵的，特别是对于大规模问题。为了打破这个瓶颈，我们通过引入对偶变换将成像问题转化为物理驱动的图像处理问题。此外，该图像处理是在重叠的块上执行的，这可以以并行或分布式方式有效地解决。这样，所提出的高分辨率成像方法可适用于大规模微波成像问题。选定的仿真结果证明了所提出的方法的最先进的性能。
英文摘要：
This paper presents an efficient algorithm of high-resolution microwave imaging based on the concept of generalized reflectivity. The contribution made in this paper is two-fold. We introduce the concept of non-parametric generalized reflectivity (GR, for short) as a function of operational frequencies and view angles, etc. The GR extends the conventional Born-based imaging model, i.e., single-scattering model, into that accounting for more realistic interaction between the electromagnetic wavefield and imaged scene. Afterwards, the GR-based microwave imaging is formulated in the convex of sparsity-regularized optimization. Typically, the sparsity-regularized optimization requires the implementation of iterative strategy, which is computationally expensive, especially for large-scale problems. To break this bottleneck, we convert the imaging problem into the problem of physics-driven image processing by introducing a dual transformation. Moreover, this image processing is performed over overlapping patches, which can be efficiently solved in the parallel or distributed manner. In this way, the proposed high-resolution imaging methodology could be applicable to large-scale microwave imaging problems. Selected simulation results are provided to demonstrate the state-of-art performance of proposed methodology.
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