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The application of radio-based positioning systems is ever increasing. Inlight of the dissemination of the Internet of Things and location-awarecommunication systems, the demands on localization architectures and amount ofpossible use cases steadily increases. While traditional radio-basedlocalization is performed by utilizing stationary nodes, whose positions areabsolutely referenced, collaborative auto-positioning methods aim to estimatelocation information without any a-priori knowledge of the node distribution.The usage of auto-positioning decreases the installation efforts oflocalization systems and therefore allows their market-wide dissemination.Since observations and position information in this scenario are correlated,the uncertainties of all nodes need to be considered. In this paper we proposea discrete Bayesian method based on a multi-dimensional histogram filter tosolve the task of robust auto-positioning, allowing to propagate historicalpositions and estimated position uncertainties, as well as lowering the demandson observation availability when compared to conventional closed-formapproaches. The proposed method is validated utilizing different multipath-,outlier and failure-corrupted ranging measurements in a static environment,where we obtain at least 58% higher positioning accuracy compared to a baselineclosed-form auto-positioning approach.

Auto-Positioning in Radio-based Localization Systems: A Bayesian Approach