Moderate Deviations of the Random Riccati Equation

Abstract: We characterize the invariant filtering measures resulting from Kalman filtering with intermittent observations (\cite{Bruno}), where the observation arrival is modeled as a Bernoulli process. In \cite{Riccati-weakconv}, it was shown that there exists a $\overline{\gamma}^{{\scriptsize{sb}}}>0$ such that for every observation packet arrival probability $\overline{\gamma}$, $\overline{\gamma}>\overline{\gamma}^{{\scriptsize{sb}}}>0$, the sequence of random conditional error covariance matrices converges in distribution to a unique invariant distribution $\mathbb{\mu}^{\overline{\gamma}}$ (independent of the filter initialization.) In this paper, we prove that, for controllable and observable systems, $\overline{\gamma}^{{\scriptsize{sb}}}=0$ and that, as $\overline{\gamma}\uparrow 1$, the family ${\mathbb{\mu}^{\overline{\gamma}}}_{\overline{\gamma}>0}$ of invariant distributions satisfies a moderate deviations principle (MDP) with a good rate function $I$. The rate function $I$ is explicitly identified. In particular, our results show: