Radar Processing

Radar processing focuses on enhancing the quality and utility of radar signals, aiming to improve accuracy and reliability in various applications. Current research emphasizes developing robust algorithms, often employing deep learning architectures like Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), to address challenges such as noise reduction, interference mitigation, and efficient data processing on resource-constrained platforms. These advancements are crucial for improving the performance of radar systems in diverse fields, including autonomous vehicles, robotics, and environmental monitoring, by enabling more accurate object detection, mapping, and gesture recognition.