Approximation of the Filter Equation for Multiple Timescale, Correlated, Nonlinear Systems

TL;DR

This paper develops an approximation method for the continuous-time filtering equation in complex systems with multiple timescales and correlations, proving convergence to a simplified averaged filter in the limit of large scale separation.

Contribution

It introduces a novel approximation approach for filtering in multi-timescale, correlated nonlinear systems, with a rigorous proof of convergence using perturbed test functions and Poisson equations.

Findings

Weak convergence of the filtering solution to an averaged filter

Handling of correlated slow-intermediate and observation processes

Application of perturbed test function method for limit analysis

Abstract

This paper considers the approximation of the continuous time filtering equation for the case of a multiple timescale (slow-intermediate, and fast scales) that may have correlation between the slow-intermediate process and the observation process. The signal process is considered fully coupled, taking values in $\mathbb{R^{m}} \times \mathbb{R^{n}}$ and without periodicity assumptions on coefficients. It is proved that in the weak topology, the solution of the filtering equation converges in probability to a solution of a lower dimensional averaged filtering equation in the limit of large timescale separation. The method of proof uses the perturbed test function approach (method of corrector) to handle the intermediate timescale in showing tightness and characterization of limits. The correctors are solutions of Poisson equations.