Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales | Zendy

Wenxuan Yao | Zendy; Jiecheng Zhao | Zendy; Micah J. Till | Zendy; Shutang You | Zendy; Yong Liu | Zendy; Yi Cui | Zendy; Yilu Liu | Zendy

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

Author(s) -

Wenxuan Yao,

Jiecheng Zhao,

Micah J. Till,

Shutang You,

Yong Liu,

Yi Cui,

Yilu Liu

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2707060

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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 that combines a wavelet-based signature extraction and feedforward artificial neural network-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.

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