Robust Large-Scale Estimation​

State estimation has been highly successful in many applications, such as mobile navigation and situational awareness. Typically, current solutions rely on relatively small-scale systems where all sensor data is available in a central node. However, the growing availability of sensory data introduces the challenge of managing large-scale sensor networks, such as fleets of vehicles, groups of drones, or numerous environmental sensors.

Current state-of-the-art methods struggle with the sheer scale, requiring excessive communication and high computational power in a single node, and are sensitive to node failures and communication issues. Additionally, as the network size increases, the risk of faulty data from failing or malicious nodes rises, and uncertain sensor quality complicates data utilization.

This project aims to develop distributed estimation methods to enhance scalability, robustness, and efficiency in large sensor networks. Key research areas include information compression, detecting misbehaving nodes, and utilizing data with uncertain quality, ultimately improving situational awareness and data utilization for various applications.