Combining Particle MCMC with Rao-Blackwellized Monte Carlo Data Association for Parameter Estimation in Multiple Target Tracking

TL;DR

This paper introduces a novel algorithm combining Rao-Blackwellized Monte Carlo data association with particle MCMC methods to jointly estimate parameters and data associations in multiple target tracking, improving efficiency and accuracy.

Contribution

The paper presents a new computational approach that integrates Rao-Blackwellized Monte Carlo data association with particle MCMC for enhanced joint parameter and data association estimation.

Findings

Effective in simulated multiple target tracking scenarios

Successfully applied to real-world brown bear population estimation

Improves inference efficiency and accuracy

Abstract

We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis-Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland.