Title :
Hidden Markov model state estimation with randomly delayed observations
Author :
Evans, Jamie S. ; Krishnamurthy, Vikram
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
fDate :
8/1/1999 12:00:00 AM
Abstract :
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
Keywords :
delays; distributed sensors; filtering theory; hidden Markov models; packet switching; random processes; recursive filters; state estimation; telecommunication networks; augmented state HMM; connectionless packet switched communications network; discrete-time hidden Markov model; distributed sensors; filtering; finite state Markov chain; hidden Markov model state estimation; measurements transmission; performance; randomly delayed observations; recursive filter; simulations; state estimation algorithms; Australia; Biomedical measurements; Communication networks; Delay effects; Delay estimation; Filters; Hidden Markov models; Sensor systems; Signal processing; State estimation;
Journal_Title :
Signal Processing, IEEE Transactions on
