Sensor Network Tomography: The Revenge of the Detected

A sensor network is deployed to detect the presence of a moving object (a target) in a surveyed region. Sensors make decisions about the presence of the target. Let us assume the target is aware of the detections it has caused, but has no idea which sensor has made which call. Can the target infer the positions of the detecting sensors? Since this is an inverse problem (of prey locating its predators), we shall refer to it as tomography. Maximum likelihood (ML) offers a solution, but it is combinatorial and therefore not of great practical interest.

Here we propose several alternatives and investigate their performances. One class of estimators looks for a nexus of detection activity: the peak, Fourier, and ESPRIT estimators fall into this class. But the best trade-off between complexity and performance seems to be trellis-based and of philosophy similar to the multi hypothesis tracker (MHT) idea for disambiguation of measurement-origin uncertainty (MOU) in target tracking.

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