Approximate Bayesian algorithms for multiple target tracking with binary sensors

TL;DR

This paper introduces an approximate Bayesian method for tracking multiple targets using binary sensors, overcoming intractable likelihood issues with likelihood-free algorithms to enable effective tracking.

Contribution

It presents a novel likelihood-free Bayesian approach tailored for binary sensor networks, addressing the challenge of intractable likelihood functions in multiple target tracking.

Findings

Effective tracking demonstrated with binary sensors

Likelihood-free algorithms outperform classical methods in this context

Method handles intractable likelihoods successfully

Abstract

In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the binary sensors (getting closer - moving away information) do not allow the use of the classical tools (e.g. Kalman Filter, Particle Filer), because the exact likelihood is intractable. To overcome this, we use the particular feature of the likelihood-free algorithms to produce an efficient multiple target tracking methodology.