Localisation of a source of biochemical agent dispersion using binary measurements

TL;DR

This paper presents a Bayesian method using importance sampling to localize a biochemical source based on binary sensor measurements, validated with real-world experiments under various wind conditions.

Contribution

It introduces a novel Bayesian approach with importance sampling for source localization using binary measurements, validated with experimental data.

Findings

Successful localization of biochemical sources in different wind conditions

Effective Bayesian framework with importance sampling for binary sensor data

Validated approach with three experimental datasets

Abstract

Using the measurements collected at a number of known locations by a moving binary sensor, characterised by an unknown threshold, the problem is to estimate the parameters of a biochemical source, continuously releasing material into the atmosphere. The solution is formulated in the Bayesian framework using a dispersion model of Poisson distributed particle encounters in a turbulent flow. The method is implemented using the importance sampling technique and successfully validated with three experimental datasets under different wind conditions.