A novel three-dimension fast radar imaging method based on far-field-approximation

Abstract

Three-dimension imaging and identification with radar has recently gained a lot of attention. Resulting from the over-simple imaging model and over-large computational complexity, the existing three-dimension radar imaging methods are limited in few scenarios. To break above bottlenecks, we introduce a novel physics-driven three-dimension fast radar imaging method based on far-field-approximation assumption, in which the whole imaging region would be decomposed with a series of overlapping-patches to accelerate the imaging speed in parallel. The proposed method has four key steps: e.g., first, the whole imaging region would be divided into a series of overlapping-patches, which indicates that the large-scale imaging problem is efficiently decomposed into a set of small-scale imaging sub-problems and can be solved in the parallel or distributed manner. Second, the dyadic Green's function based on far-field-approximation will be applied to construct system response function of radar imaging problem. Third, a dual transform is further introduced to turn the imaging problem into the problem of physical-driven image processing problem. In this way, the image processing system response functions for all overlapping-patches will be identical for fixed transmitters and receivers. In other words, the system response functions of overlapping-patches are independent of specific imaging regions. So few system response functions matrices need be memorized. At last, the first-order method is introduced to implement the imaging process of all overlapping-patches in parallel. The far-field-approximation imaging method drastically decreases the memory requirement while maintaining fast imaging speed and high imaging quality. Therefore, the proposed fast imaging method based on far-field-approximation is applicable to more large-scale imaging scenarios. Some selected simulation results are presented to demonstrate the state-of-the-art performance of the far-field-approximation methods.