Quenched large deviations for one dimensional nonlinear filtering

Abstract

Consider the standard, one dimensional, nonlinear filtering problem for a diffusion processe $\Xi_t$ observed in small additive white noise. Denote by $q^\epsilon_1(\cdot)$ the density of the law of $\Xi_1$ conditioned on $\sigma(Y_t^\epsilon: 0\leq t\leq 1)$. We provide "quenched" large deviation estimates for the random family of measures $q^\epsilon_1(x)dx$: there exists a continuous, explicit mapping $\bar J : R^2\to R$ such that for almost all $B_\cdot,V_\cdot$, $\bar J(\cdot,X_1)$ is a good rate function and for any measurable $G\subset R$, $$-\inf_{x\in G^o} \bar J(x,X_1) \leq \liminf \epsilon \log \int_G q_1^\epsilon(x) dx \leq \limsup \epsilon \log \int_G q_1^\epsilon(x) dx \leq -\inf_{x\in \bar G} \bar J(x,X_1) .$$