Error Estimates of the Gain Approximation by Hermite-Galerkin Method in Feedback Particle Filter

TL;DR

This paper introduces a spectral Hermite-Galerkin method with error bounds for approximating the gain function in feedback particle filters, improving accuracy and efficiency.

Contribution

A novel two-step Hermite-Galerkin spectral approach with rigorous error estimates for gain approximation in feedback particle filters.

Findings

Kernel approximation error decays as $O(N_p^{-rac{s}{2s+1}})$

Spectral approximation error converges at $O(M^{-s+1} ext{log} M)$

Numerical experiments outperform existing schemes in accuracy and efficiency

Abstract

The feedback particle filter (FPF) is a promising nonlinear filtering (NLF) method, but its practical implementation is hindered by the intractability of the gain function, which satisfies a boundary value problem (BVP). This paper proposes a novel two-step Hermite-Galerkin spectral method to address this challenge. First, the unknown density in the BVP is approximated by a kernel density estimator, whose error bounds are well-established in the literature. Second, rather than directly approximating the gain function, we approximate an auxiliary variable via the Galerkin spectral method using generalized Hermite functions. This auxiliary variable inherits the rapid decay property of the density at infinity, which aligns perfectly with the exponential decay characteristic of generalized Hermite functions, thereby obviating the need for artificial boundary conditions or domain truncation. Furthermore, we rigorously establish two fundamental error estimates: the kernel approximation error decays at the rate $O(N_p^{-\frac{s}{2s+1}})$, while the spectral approximation error converges at $O(M^{-s+1}\log M)$, providing complete theoretical guarantees for the method's accuracy. Comprehensive numerical experiments validate the theoretical results and demonstrate that the proposed method outperforms existing gain approximation schemes in both accuracy and computational efficiency.