# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 1 Introduction  
With the development of information technology, an increasing number of intelligent applications have started to appear. Within this trend, a large number of intelligent applications build intelligent systems, such as the \"smart city\" mentioned in the literature [1], which can maximize the use of limited resources to manage the city with high quality. However, as an intelligent system, it needs enough information to make reasonable judgments, but information is diverse; sounds, pictures, and smells are all information, and to obtain them is just like the human sensing process. Thus, various sensors are designed to implement a certain function of human sensing; for example, a camera is like an eye to get visual information. However, intelligent systems are not limited to this; they can see much more. While cameras are affected by lighting conditions and do not have the ability to penetrate, there are technologies that are not affected by lighting and can penetrate the surface to see the essence, such as X-rays, which can see the state of a patient's internal organs. The literature [2, 3] illustrates the value that this imaging technique brings to intelligent systems, where capturing the information, aided by AI techniques, allows for a quick and accurate determination of the disease. Its non-contact characteristic is more valuable in the background of COVID-19 ravaging the world. However, X-rays are expensive and radioactive, limiting the environment in which they can be applied. Therefore, intelligent systems need cheaper and safer RF imaging technology to meet a wider range of application environments. As an advanced application in the field of wireless sensing, RF imaging has received a lot of attention, and the introduction of \"wireless vision\" in [4] indicates that RF imaging has become a new research hotspot in the field of wireless sensing, which requires finer sensing granularity than most sensing fields. However, the development of RF imaging has not been smooth and has long been constrained by insufficient imaging resolution and aperture. Currently, mmWave-based SAR imaging technology has the advantages of greater imaging bandwidth and stronger imaging aperture expansion, and has become one of the enabling technologies for RF imaging to meet fine-grained sensing needs in near-field conditions. There have been many studies based on millimeter-wave SAR imaging technology. Various millimeter-wave SAR near-field imaging systems developed in the literature [5; 6; 7; 8; 9; 10] were successfully used for security screening. The authors of [11] developed a simple 77 GHz millimeter wave SAR near-field imaging system using a commercial millimeter-wave radar and rails. In the literature [12], a low-cost 24 GHz millimeter-wave SAR near-field imaging system was implemented using Hilbert transform to compensate for the real signal. Although the available studies have reduced the system cost and improved the imaging quality, there are still some challenges. SAR uses the time-for-space tactics, which makes its data acquisition time long. Sometimes, in order to quickly verify the imaging method, data have to be acquired under Sparsely-Sampled conditions. This generates ghost image and is not conducive to the analysis of imaging results, so it is necessary to suppress ghost image under Sparsely-Sampled conditions. The authors of [13] solve the Sparsely-Sampled ghost image problem under MIMO (Multiple Input Multiple Output) conditions, but does not address the problem under SISO (Single Input Single Output) conditions. The work in this paper still addresses the case of uniform sampling. In addition to the uniform sampling technique, the authors of [14] use a non-uniform hybrid sampling technique to construct a SAR imaging system that achieves high-resolution imaging. In addition, the work in this paper is performed for the Analytic Fourier Transform algorithm, while in [15] it is performed for the Frequency Scaling algorithm (FSA), and the FSA is improved to eliminate the impact of the aliasing effect.