An Efficient Meshfreee Implicit Filter for Nonlinear Filtering Problems

TL;DR

This paper introduces a meshfree approximation method to enhance the efficiency of the implicit filter for nonlinear filtering problems, especially in moderately high-dimensional spaces, using random points and meshfree interpolation.

Contribution

The paper presents a novel meshfree approximation technique that improves the computational efficiency of the implicit filter in nonlinear filtering tasks.

Findings

Demonstrates improved efficiency over traditional methods

Effective in moderately high-dimensional filtering problems

Numerical experiments confirm the method's effectiveness

Abstract

In this paper, we propose a meshfree approximation method for the implicit filter developed in [2], which is a novel numerical algorithm for nonlinear filtering problems. The implicit filter approximates conditional distributions in the optimal filter over a deterministic state space grid and is developed from samples of the current state obtained by solving the state equation implicitly. The purpose of the meshfree approximation is to improve the efficiency of the implicit filter in moderately high-dimensional problems. The construction of the algorithm includes generation of random state space points and a meshfree interpolation method. Numerical experiments show the effectiveness and efficiency of our algorithm.