Convergence Results for Continuous-Time Adaptive Stochastic Filtering Algorithms

摘要

The adaptive stochastic filtering problem for Gaussian processes is considered. The selftuning synthesis procedure is used to derive two algorithms for this problem. Almost sure convergence for the parameter estimate and the filtering error is established. The convergence analysis is based on the almost-supermartingale convergence lemma that allows a stochastic Lyapunov like approach.