Estimating the Static Parameters in Linear Gaussian Multiple Target Tracking Models

TL;DR

This paper develops offline and online maximum likelihood estimation methods, including EM algorithms and Monte Carlo approximations, for inferring static parameters in linear Gaussian multiple target tracking models, validated through simulations.

Contribution

It introduces both batch and online EM algorithms with Monte Carlo methods for parameter estimation in MTT models, enhancing existing approaches.

Findings

EM algorithms effectively estimate parameters in simulated scenarios.

Monte Carlo methods improve approximation accuracy.

Comparison shows competitive performance with Bayesian methods.

Abstract

We present both offline and online maximum likelihood estimation (MLE) techniques for inferring the static parameters of a multiple target tracking (MTT) model with linear Gaussian dynamics. We present the batch and online versions of the expectation-maximisation (EM) algorithm for short and long data sets respectively, and we show how Monte Carlo approximations of these methods can be implemented. Performance is assessed in numerical examples using simulated data for various scenarios and a comparison with a Bayesian estimation procedure is also provided.