Millimeter-wave radar for intelligent sensing: A comprehensive review of techniques, applications, and challenges

Abstract

Millimeter-wave (mmWave) radar sensing has established itself as a robust technology across diverse applications, such as automotive, healthcare, security, and smart homes. Its exceptional capacity to function effectively in varying environmental conditions, detect concealed objects, sense physiological signals, and facilitate precise target detection positions it as a pivotal enabler for next-generation sensing solutions. The survey employs bibliometric analysis to critically evaluate the existing literature surrounding mmWave radar, highlighting key research trends, notable publications, and the challenges faced within the field. This work presents a comprehensive examination of mmWave radar-based sensing, detailing its fundamental operating principles, signal processing methodologies, advancements in hardware, and the latest developments in machine learning applications. It also addreses the key challenges in signal processing, including resolution enhancement, environmental adaptability, and data fusion with complementary sensors such as LiDAR and cameras. Furthermore, explored the potential of deep learning techniques to enhance target classification, activity recognition, gesture identification, and healthcare applications while addressing concerns related to accuracy and precision. This survey also sheds light on emerging trends by assessing the strengths, limitations, and prospects of mmWave radar technology. This review aims to provide insightful guidance for researchers and practitioners committed to advancing radar-based sensing and its real-world implementations.