When building distributed sensor networks with incomplete data, combining state observers with cooperative Gaussian Process learning can accurately estimate both what's happening in the system and how it behaves, with proven error bounds.
This paper presents a method for distributed systems with multiple sensors to estimate both system states and unknown dynamics when only partial measurements are available. It combines observer-based estimation with online Gaussian Process regression across a network, includes a smart data collection strategy, and provides theoretical guarantees on estimation accuracy.