Discrete and Hybrid Stochastic State Estimation Algorithms for Networked Control Systems
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
On the memory complexity of the forward-backward algorithm
Pattern Recognition Letters
Stochastic observability in network state estimation and control
Automatica (Journal of IFAC)
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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We address the problem of filtering and fixed-lag smoothing for discrete-time and discrete-state hidden Markov models (HMMs), with the intention of extending some important results in Kalman filtering, notably the property of exponential stability. By appealing to a generalized Perron-Frobenius result for non-negative matrices, we are able to demonstrate exponential forgetting for both the recursive filters and smoothers; furthermore, methods for deriving overbounds on the convergence rate are indicated. Simulation studies for a two-state and two-output HMM verify qualitatively some of the theoretical predictions, and the observed convergence rate is shown to be bounded in accordance with the theoretical predictions