Title :
A comparison between optimal and Kalman filtering for hidden Markov processes
Author :
White, Langford B.
Author_Institution :
Div. of Commun., Defence Sci. & Technol. Organ., Salisbury, SA, Australia
fDate :
5/1/1998 12:00:00 AM
Abstract :
This paper gives sufficient conditions for specifying the optimal linear filters for a hidden Markov process (HMP) and compares its performance with the optimal (i.e., minimum conditional variance) filter derived from the corresponding hidden Markov model using a simulation. The optimal filter performs much better at high signal-to-noise ratio (SNR) but the performance loss using the linear filter reduces as the SNR decreases.
Keywords :
Kalman filters; circuit optimisation; filtering theory; hidden Markov models; linear systems; noise; statistical analysis; Kalman filtering; SNR; hidden Markov model; hidden Markov processes; high signal-to-noise ratio; linear system; minimum conditional variance filter; optimal filtering; optimal linear filters; performance; second order statistics; simulation; sufficient conditions; Covariance matrix; Filtering; Gaussian processes; Hidden Markov models; Kalman filters; Linear systems; Nonlinear filters; State estimation; Statistics; Sufficient conditions;
Journal_Title :
Signal Processing Letters, IEEE
