Radar Signal Processing

Radar signal processing focuses on extracting meaningful information from radar data to enable applications like autonomous driving and fall detection. Current research emphasizes the use of deep learning, particularly convolutional and recurrent neural networks, and transformers, to improve object classification, beam prediction, and super-resolution imaging, often incorporating multimodal data fusion with other sensors like cameras and LiDAR. These advancements are driven by the need for more accurate, efficient, and robust radar systems for safety-critical applications, with a growing focus on optimizing algorithms for deployment on embedded hardware.