Nonlinear Stochastic Filtering with Volterra Gaussian noises

Abstract

We develop a nonlinear filtering theory for signal-observation systems driven by Volterra Gaussian processes, covering both the Young and genuinely rough regimes. The dynamics are formulated as a rough differential equation in which the observation has a signal-dependent Volterra drift, a structure naturally induced by an equivalent change of measure. We establish global well-posedness of the coupled system and derive a Kallianpur-Striebel formula. We then obtain a robust pathwise representation of the filter. In the one-dimensional setting, we characterise the unnormalised conditional density through a rough Zakai equation and establish its well-posedness using an extension of the rough viscosity framework. Finally, under a partial H\"ormander-type condition, we prove that the conditional distribution of the signal admits a smooth density.