Radar Signal
Radar signals are electromagnetic waves used to detect and characterize objects by analyzing their reflections. Current research focuses on improving signal processing techniques, particularly using deep learning models like Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), to address challenges such as noise reduction, artifact removal, and improved target localization. These advancements are driving progress in diverse applications, including contactless medical monitoring (e.g., ECG reconstruction from radar), autonomous driving (e.g., object detection and road boundary estimation), and surveillance (e.g., human pose estimation and activity classification). The development of robust and efficient radar signal processing methods is crucial for enhancing the capabilities and reliability of numerous technologies.
Papers
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network
Muhammad Uzair Zahid, Serkan Kiranyaz, Alper Yildirim, Moncef Gabbouj
Non-Contact Breath Rate Classification Using SVM Model and mmWave Radar Sensor Data
Mohammad Wassaf Ali, Ayushi Gupta, Mujeev Khan, Mohd Wajid