Hidden Markov model filtering over packet switched networks

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 modelled as a finite state Markov chain which allows an augmented state HMM to model the overall system. State estimation algorithms for the resultant HMM are then presented. The motivation for the model stems from the situation when distributed sensors transmit measurement over a connectionless packet switched communications network.