Classification of CommSense data using learning algorithms

Bhatta, Abhishek; Mishra, Amit Kumar; Pidanič, Jan

doi:10.1049/cp.2017.0436

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2017

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“…For large F the inner products of the feature space can be calculated by a kernel function, k(x, y) = φ(x), φ(y) . Detailed explanation regarding implementation of SVM on CommSense system is given in [5].…”

Section: Descriptionmentioning

confidence: 99%

GSM–CommSense-based through-the-wall sensing

Bhatta

Mishra

2017

Remote Sensing Letters

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We have recently proposed a scheme to use the channel equalization blocks of telecommunication systems to sense changes in an environment. We call this communication-sensing, CommSense for short.After some initial positive results we tried to use our global system for mobile communication (GSM)based CommSense system for a through-the-wall sensing application. As the system was inherently highly under-determined we used statistical machine learning techniques to help us sense environmental changes in the behind-the-wall experiments. We observed that with limited amount of data per GSM frame of 577 µs a person can be detected across a wall to an accuracy of 77.458% and a person carrying a weapon can be detected to an accuracy of 95.208%. We present details of the experiments and the encouraging results that we have obtained in this article.

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confidence: 99%