Kalman filtering: theory and practice
Kalman filtering: theory and practice
Variational Learning for Switching State-Space Models
Neural Computation
State estimation and fault diagnosis of industrial process by using of particle filters
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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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. Diagnostic systems for such applications must address new challenges caused by the distribution of resources, the networking environment, and the tight coupling between the computational and physical worlds. Our approach is to move from centralized, discrete or continuous techniques toward a distributed, hybrid diagnosis architecture. Monitoring and diagnosis of any dynamical system depend crucially on the ability to estimate the system state given the observations. Estimation for 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 on estimation algorithm that addresses the challenge of the interaction between continuous and discrete dynamics in hybrid systems.