Digital Signal Processing
State estimation and detectability of probabilistic discrete event systems
Automatica (Journal of IFAC)
Signal estimation with multiple delayed sensors using covariance information
Digital Signal Processing
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This paper considers state estimation for a discrete-time hidden Markov model (HMM) when the observations are delayed by a random time. The delay process is itself modeled as a finite state Markov chain that allows an augmented state HMM to model the overall system. State estimation algorithms for the resulting HMM are then presented, and their performance is studied in simulations. The motivation for the model stems from the situation when distributed sensors transmit measurements over a connectionless packet switched communications network