Remote Estimation

Remote estimation focuses on accurately reconstructing a system's state from data collected remotely, aiming to minimize estimation errors under various constraints like limited bandwidth or noisy channels. Current research emphasizes developing optimal transmission scheduling policies, often employing deep reinforcement learning (DRL) algorithms enhanced by incorporating structural properties of optimal solutions to improve efficiency and accuracy. These advancements are crucial for applications ranging from industrial process monitoring and precision agriculture (e.g., using InSAR data for geologic composition estimation) to robotics and large-scale distributed systems, where efficient and reliable remote state estimation is paramount.