Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales
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...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2017-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/7932434/ |
id |
doaj-6a2a66255728478f89cf845147d2c465 |
---|---|
record_format |
Article |
spelling |
doaj-6a2a66255728478f89cf845147d2c4652021-03-29T20:01:20ZengIEEEIEEE Access2169-35362017-01-015111661117510.1109/ACCESS.2017.27070607932434Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic ScalesWenxuan Yao0https://orcid.org/0000-0002-5011-2196Jiecheng Zhao1https://orcid.org/0000-0002-1186-8281Micah J. Till2Shutang You3https://orcid.org/0000-0003-3158-3643Yong Liu4https://orcid.org/0000-0002-6714-9411Yi Cui5https://orcid.org/0000-0002-0973-708XYilu Liu6Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USADepartment of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USADepartment of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USADepartment of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USADepartment of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USADepartment of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USADepartment of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USAThe 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.https://ieeexplore.ieee.org/document/7932434/Distribution-levelENF signalfrequency measurementsignature extractionlocation identification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenxuan Yao Jiecheng Zhao Micah J. Till Shutang You Yong Liu Yi Cui Yilu Liu |
spellingShingle |
Wenxuan Yao Jiecheng Zhao Micah J. Till Shutang You Yong Liu Yi Cui Yilu Liu Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales IEEE Access Distribution-level ENF signal frequency measurement signature extraction location identification |
author_facet |
Wenxuan Yao Jiecheng Zhao Micah J. Till Shutang You Yong Liu Yi Cui Yilu Liu |
author_sort |
Wenxuan Yao |
title |
Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales |
title_short |
Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales |
title_full |
Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales |
title_fullStr |
Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales |
title_full_unstemmed |
Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales |
title_sort |
source location identification of distribution-level electric network frequency signals at multiple geographic scales |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2017-01-01 |
description |
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. |
topic |
Distribution-level ENF signal frequency measurement signature extraction location identification |
url |
https://ieeexplore.ieee.org/document/7932434/ |
work_keys_str_mv |
AT wenxuanyao sourcelocationidentificationofdistributionlevelelectricnetworkfrequencysignalsatmultiplegeographicscales AT jiechengzhao sourcelocationidentificationofdistributionlevelelectricnetworkfrequencysignalsatmultiplegeographicscales AT micahjtill sourcelocationidentificationofdistributionlevelelectricnetworkfrequencysignalsatmultiplegeographicscales AT shutangyou sourcelocationidentificationofdistributionlevelelectricnetworkfrequencysignalsatmultiplegeographicscales AT yongliu sourcelocationidentificationofdistributionlevelelectricnetworkfrequencysignalsatmultiplegeographicscales AT yicui sourcelocationidentificationofdistributionlevelelectricnetworkfrequencysignalsatmultiplegeographicscales AT yiluliu sourcelocationidentificationofdistributionlevelelectricnetworkfrequencysignalsatmultiplegeographicscales |
_version_ |
1724195410228019200 |