Radio Based

Radio-based localization uses wireless signals to determine the position of devices or objects, aiming to improve accuracy and robustness in various environments, particularly where GPS is unavailable. Current research heavily utilizes deep learning, particularly convolutional neural networks, and meta-learning techniques to improve the generalization of models across different environments and signal characteristics, often incorporating additional sensor data (e.g., visual or inertial) for enhanced performance. This field is significant for applications ranging from indoor robotics and warehouse management to autonomous vehicle navigation and drone tracking, driving advancements in both machine learning and wireless communication technologies.