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Device-free passive (DfP) localization is a system envisioned to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. A DfP system allows using nominal WiFi equipment for intrusion detection, without using any extra hardware, adding smartness to any WiFi-enabled device. In this paper, we focus on the detection function of the DfP system in a real environment. We show that the performance of our previously developed algorithms for detection in a controlled environments, which achieved 100% recall and precision, degrades significantly when tested in a real environment. We present an alternative algorithm, based on the maximum likelihood estimator (MLE), that has a significant performance increase in a real environment. Our results show that the recall of the system increases by more than 10% when using the proposed MLE without affecting the system's precision.
Moussa et al. (Sun,) studied this question.