Micro Doppler

Micro-Doppler radar analyzes the subtle frequency shifts in radar signals caused by moving targets, primarily focusing on extracting information about human activities and vital signs. Current research emphasizes improving the accuracy and robustness of micro-Doppler signal processing using deep learning techniques, such as convolutional neural networks, recurrent neural networks, and transformers, often incorporating novel loss functions or input representations to enhance performance. These advancements are driving progress in applications like through-the-wall human activity recognition, remote vital sign monitoring, and improved pedestrian identification, particularly in scenarios with limited data or noisy environments.