Maximum likelihood diffusive source localization based on binary observations

Abstract

In this paper, we construct the maximum likelihood (ML) estimator of diffusive source location based on binary observations. We utilize two different estimation approaches, ML estimation based on all the observations (i.e.. batch processing) and approximated ML estimation using only new observations and the previous estimate (i.e., real time processing). The performance of these estimators are compared with theoretical bounds and are shown to achieve excellent performance.