  Accelerated by the increasing attention drawn by 5G, 6G, and Internet of
Things applications, communication and sensing technologies have rapidly
evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years.
Enabled by significant advancements in electromagnetic (EM) hardware, mmWave
and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz,
respectively, can be employed for a host of applications. The main feature of
THz systems is high-bandwidth transmission, enabling ultra-high-resolution
imaging and high-throughput communications; however, challenges in both the
hardware and algorithmic arenas remain for the ubiquitous adoption of THz
technology. Spectra comprising mmWave and THz frequencies are well-suited for
synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide
spectrum of tasks like material characterization and nondestructive testing
(NDT). This article provides a tutorial review of systems and algorithms for
THz SAR in the near-field with an emphasis on emerging algorithms that combine
signal processing and machine learning techniques. As part of this study, an
overview of classical and data-driven THz SAR algorithms is provided, focusing
on object detection for security applications and SAR image super-resolution.
We also discuss relevant issues, challenges, and future research directions for
emerging algorithms and THz SAR, including standardization of system and
algorithm benchmarking, adoption of state-of-the-art deep learning techniques,
signal processing-optimized machine learning, and hybrid data-driven signal
processing algorithms...
