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Passive radio frequency (PRF)-based indoor positioning systems (IPS) haveattracted researchers' attention due to their low price, easy and customizableconfiguration, and non-invasive design. This paper proposes a PRF-basedthree-dimensional (3D) indoor positioning system (PIPS), which is able to usesignals of opportunity (SoOP) for positioning and also capture a scenariosignature. PIPS passively monitors SoOPs containing scenario signatures througha single receiver. Moreover, PIPS leverages the Dynamic Data DrivenApplications System (DDDAS) framework to devise and customize the samplingfrequency, enabling the system to use the most impacted frequency band as therated frequency band. Various regression methods within three ensemble learningstrategies are used to train and predict the receiver position. The PRFspectrum of 60 positions is collected in the experimental scenario, and threecriteria are applied to evaluate the performance of PIPS. Experimental resultsshow that the proposed PIPS possesses the advantages of high accuracy,configurability, and robustness.

Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning