Estimation of distributed hybrid systems using particle filtering methods

Networked embedded systems are composed of a large number of components that interact with the physical world via a set of sensors and actuators, have their own computational capabilities, and communicate with each other via a wired or wireless network. Such systems are best modeled by distributed hybrid systems that capture the interaction between the physical and computational components. Monitoring and diagnosis of any dynamical system depend crucially on the ability to estimate the system state given the observations. Estimation for distributed hybrid systems is particularly challenging because it requires keeping track of multiple models and the transitions between them. This paper presents a particle filtering based estimation algorithm for a class of distributed hybrid systems. The hybrid estimation methodology is demonstrated on a cryogenic propulsion system.