Feasibility Study on Intra-Grid Location Estimation Using Power ENF Signals

Ravi Garg, Adi Hajj-Ahmad, Min Wu
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引用次数: 9

Abstract

The Electric Network Frequency (ENF) is a signature of power distribution networks that can be captured by multimedia recordings made in areas where there is electrical activity. This has led to an emergence of several forensic applications based on the use of the ENF signature. Examples of such applications include estimating or verifying the time-of-recording of a media signal and inferring the power grid associated with the location in which the media signal was recorded. In this paper, we carry out a feasibility study to examine the possibility of using embedded ENF traces to pinpoint the location-of-recording of a signal within a power grid. In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces. To this purpose, a high-passed version of an ENF signal is extracted and it is demonstrated that the correlation between two such signals, extracted from recordings made in different geographical locations within the same grid, decreases as the distance between the recording locations increases. We harness this property of correlation in the ENF signals to propose trilateration based localization methods, which pinpoint the unknown location of a recording while using some known recording locations as anchor locations. We also discuss the challenges that need to be overcome in order to extend this work to using ENF traces in noisier audio/video recordings for such fine localization purposes.
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基于功率ENF信号的网格内位置估计可行性研究
电网频率(ENF)是配电网络的一个特征,可以通过在有电活动的区域进行的多媒体记录来捕获。这导致了基于使用ENF签名的几个取证应用程序的出现。这种应用的示例包括估计或验证媒体信号的记录时间和推断与记录媒体信号的位置相关联的电网。在本文中,我们进行了可行性研究,以检查使用嵌入式ENF走线来确定电网内信号记录位置的可能性。在这项研究中,我们证明了使用包含强ENF痕迹的功率信号记录可以将记录位置精确到一定的地理分辨率。为此,提取了一个ENF信号的高通版本,结果表明,从同一网格内不同地理位置的记录中提取的两个这样的信号之间的相关性随着记录位置之间的距离增加而降低。我们利用ENF信号中的这种相关性提出了基于三边测量的定位方法,该方法可以精确定位录音的未知位置,同时使用一些已知的录音位置作为锚点位置。我们还讨论了需要克服的挑战,以便将这项工作扩展到在嘈杂的音频/视频记录中使用ENF跟踪以实现如此精细的定位目的。
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