A Drift Homotopy Implicit Particle Filter Method for Nonlinear Filtering problems

TL;DR

This paper introduces a drift homotopy implicit particle filter that enhances nonlinear filtering by combining drift homotopy with an implicit resampling approach, demonstrating improved efficiency and effectiveness through numerical experiments.

Contribution

It presents a novel drift homotopy implicit particle filter that improves resampling efficiency in nonlinear filtering tasks.

Findings

Demonstrates improved filtering accuracy.

Shows increased computational efficiency.

Validates effectiveness through numerical experiments.

Abstract

In this paper, we develop a drift homotopy implicit particle filter method. The methodology of our approach is to adopt the concept of drift homotopy in the resampling procedure of the particle filter method for solving the nonlinear filtering problem, and we introduce an implicit particle filter method to improve the efficiency of the drift homotopy resampling procedure. Numerical experiments are carried out to demonstrate the effectiveness and efficiency of our drift homotopy implicit particle filter.