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Source Location Identification of Distribution-level Electric Network Frequency Signals at Multiple Geographic Scales


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Abstract


The distribution-level electric network frequency (ENF) extracted from an electric power signal is a promising forensic tool for multimedia recording authentication. Local characteristics in ENF signals recorded in different locations act as environmental signatures, which can be potentially used as a fingerprint for location identification. In this paper, a reference database is established for distribution-level ENF using FNET/GridEye system. An ENF identification method combining a wavelet-based signature extraction and feed-forward artificial neural network (F-ANN) based machine learning, is presented to identify the location of unsourced ENF signals without relying on the availability of concurrent signals. Experiments are performed to validate the effectiveness of the proposed method using ambient frequency measurements at multiple geographic scales. Identification accuracy is presented and the factors that affect identification performance are discussed.

KeyWords
Distribution-level, ENF signal, Frequency measurement, Signature extraction, Location Identification



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