A drift homotopy Monte Carlo approach to particle filtering for multi-target tracking

TL;DR

This paper introduces a drift homotopy Monte Carlo method to enhance particle filtering in multi-target tracking, effectively improving accuracy in both linear and nonlinear observation scenarios.

Contribution

It proposes a novel drift homotopy-based Markov Chain Monte Carlo step integrated into particle filters for better tracking accuracy.

Findings

Significant performance improvement over standard particle filters

Effective in both linear and nonlinear observation models

Enhanced target-observation association handling

Abstract

We present a novel approach for improving particle filters for multi-target tracking. The suggested approach is based on drift homotopy for stochastic differential equations. Drift homotopy is used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the observations. Also, we present a simple Metropolis Monte Carlo algorithm for tackling the target-observation association problem. We have used the proposed approach on the problem of multi-target tracking for both linear and nonlinear observation models. The numerical results show that the suggested approach can improve significantly the performance of a particle filter.