Abstract
Radio frequency (RF) spectrum sensing is critical for applications requiring precise object and posture detection and classification. This survey aims to provide a focused review of context-aware RF-based sensing, emphasizing its principles, advancements, and challenges. It specifically examines state-of-the-art techniques such as phased array radar, synthetic aperture radar, and passive RF sensing, highlighting their methodologies, data input domains, and spatial diversity strategies. The paper evaluates feature extraction methods and machine learning approaches used for detection and classification, presenting their accuracy metrics across various applications. Additionally, it investigates the integration of RF sensing with other modalities, such as inertial sensors, to enhance context awareness and improve performance. Challenges like environmental interference, scalability, and regulatory constraints are addressed, with insights into real-world mitigation strategies. The survey concludes by identifying emerging trends, practical applications, and future directions for advancing RF sensing technologies.