Approximations for nonlinear filters based on quantisation
The author studies the use of observation quantisation with discrete-time nonlinear filters in which the signal is a finite-state Markov chain, and the observation is the signal plus white noise. It is shown that the reduction of the filter accuracy is small for filters with slow signal dynamics and substantially noisy observations. A version of this result for continuous-time filters is presented. Optimal quantisation schemes are also discussed. At the end, three numerical methods for continuous-time Markov chain filters (including deterministic and stochastic particle methods based on quantised observations) are compared.
scientific article; zbMATH DE number 1623585