Range Doppler

Range-Doppler processing analyzes radar signals to extract target range and velocity information, crucial for applications like object detection and tracking. Current research emphasizes improving the accuracy and robustness of this analysis, particularly in cluttered environments, using machine learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers, often applied to range-Doppler maps or directly to raw radar data. These advancements are driving improvements in diverse fields, including autonomous driving, fall detection for elderly care, and gesture recognition for human-computer interaction. The focus is on developing efficient and accurate algorithms that can operate in real-time on resource-constrained devices.