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

TL;DR

This paper introduces a fast, high-resolution microwave imaging algorithm based on a non-parametric generalized reflectivity model, extending traditional models and enabling efficient large-scale imaging through parallel processing.

Contribution

It proposes a novel non-parametric generalized reflectivity model and a physics-driven image processing approach that significantly reduces computational complexity for large-scale microwave imaging.

Findings

Demonstrates state-of-the-art imaging performance in simulations.

Enables efficient processing of large-scale problems.

Improves realism over traditional single-scattering models.

Abstract

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.