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This paper presents an efficient algorithm of high-resolution microwaveimaging based on the concept of generalized reflectivity. The contribution madein this paper is two-fold. We introduce the concept of non-parametricgeneralized reflectivity (GR, for short) as a function of operationalfrequencies and view angles, etc. The GR extends the conventional Born-basedimaging model, i.e., single-scattering model, into that accounting for morerealistic interaction between the electromagnetic wavefield and imaged scene.Afterwards, the GR-based microwave imaging is formulated in the convex ofsparsity-regularized optimization. Typically, the sparsity-regularizedoptimization requires the implementation of iterative strategy, which iscomputationally expensive, especially for large-scale problems. To break thisbottleneck, we convert the imaging problem into the problem of physics-drivenimage processing by introducing a dual transformation. Moreover, this imageprocessing is performed over overlapping patches, which can be efficientlysolved in the parallel or distributed manner. In this way, the proposedhigh-resolution imaging methodology could be applicable to large-scalemicrowave imaging problems. Selected simulation results are provided todemonstrate the state-of-art performance of proposed methodology.

Fast Algorithm of High-resolution Microwave Imaging Using the Non-parametric Generalized Reflectivity Model