Path sampling for particle filters with application to multi-target tracking

TL;DR

This paper introduces a new Markov Chain Monte Carlo method to enhance particle filters for multi-target tracking, demonstrating significant performance improvements over previous approaches in both linear and nonlinear models.

Contribution

The paper proposes an alternative MCMC approach to improve particle filter accuracy in multi-target tracking, extending prior drift homotopy methods.

Findings

Significant performance improvement in particle filters.

Effective on both linear and nonlinear observation models.

New MCMC approach enhances tracking accuracy.

Abstract

In recent work (arXiv:1006.3100v1), we have presented a novel approach for improving particle filters for multi-target tracking. The suggested approach was based on drift homotopy for stochastic differential equations. Drift homotopy was 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. In the current work, we present an alternative way to append a Markov Chain Monte Carlo step to a particle filter to bring the particle filter samples closer to the observations. Both current and previous approaches stem from the general formulation of the filtering problem. We have used the currently 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.