Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.

翻译：毫米波（30 GHz至300 GHz）与太赫兹（300 GHz至10 THz）传感应用日益受到关注，涵盖安防检测、工业包装、医学成像及无损检测等领域。基于新型数据驱动算法的感知与成像方法在分辨率、定位精度及检测率方面取得突破，对传统技术构成挑战。过去十年间，深度学习技术在感知与计算机视觉领域获得了广泛关注。尽管传统信号处理技术更易推广至各类应用场景，但信号处理与学习型算法交织的混合方法在性能与泛化能力之间提供了颇具前景的平衡方案。此类混合算法通过利用射频波形的已知特性优化模型训练，从而比传统方法更高效地训练深度学习算法并实现更优性能。本文提出用于改进毫米波成像系统的新型混合学习算法，可广泛应用于感知与传感领域的多种问题。研究涵盖多个问题空间，包括静态与动态手势分类、人机交互中的手部精确定位、基于前视合成孔径雷达的高分辨率近场毫米波成像、非规则扫描几何条件下的SAR成像、基于深度神经网络与视觉Transformer架构的毫米波图像超分辨率重建，以及采用新型混合学习架构的数据级多波段雷达融合。此外，本文还提出若干深度学习模型训练与数据集合成的创新方法。