Source Localisation Using Binary Measurements

TL;DR

This paper develops a Bayesian localization method for stationary sources using mobile agents with binary measurements, incorporating detection probabilities and optimizing agent placement, with proven convergence and demonstrated effectiveness.

Contribution

It introduces a Bayesian estimation framework with analytical convergence for localizing sources using binary data, and optimizes agent configurations based on Fisher Information.

Findings

Algorithm converges reliably in simulations

Optimal agent geometries improve localization accuracy

Robustness to inexact detection probability knowledge

Abstract

This paper considers the problem of localising a stationary signal source using a team of mobile agents which only take binary measurements. Background false detection rates and missed detection probabilities are incorporated into the framework. A Bayesian estimation algorithm that discretises the search environment is employed, and analytical convergence and consistency results for this are derived. Fisher Information is then used as a metric for the design of optimal agent geometries. Knowledge of the probability of detection as a function of the source and agent locations is assumed in the analysis, with special attention given to range-dependent functions. The behaviour of the algorithm under inexact knowledge of the probability of detection is also analysed. Finally, simulation results are presented to demonstrate the effectiveness of the algorithm.