Iterative classification strategy for multi-resolution wireless sensing of passive targets

Abstract : In this Letter, the estimation of the presence, position, and posture of a device-free target is addressed through a multi-resolution strategy that applies a virtual zoom on the information content of the channel state information measured by a single WiFi link. A series of binary classifiers are trained to estimate multiple location-based features of the target from the same measurement. Preliminary experiments point out the feasibility to estimate high-resolution features such as the target posture even in very large investigation domains, passing through the estimation of the target presence and position. A robust and ubiquitous wireless sensing is obtained with failure rates lower than 2.5% in the three considered resolution steps.
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Article dans une revue
Electronics Letters, IET, 2018, 54 (2), pp.101 - 103. 〈10.1049/el.2017.2036〉
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https://hal.archives-ouvertes.fr/hal-01767442
Contributeur : Andrea Massa <>
Soumis le : lundi 16 avril 2018 - 11:24:31
Dernière modification le : mercredi 18 avril 2018 - 01:17:40

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Federico Viani, Marco Donald Migliore, Alessandro Polo, Marco Salucci, Andrea Massa. Iterative classification strategy for multi-resolution wireless sensing of passive targets. Electronics Letters, IET, 2018, 54 (2), pp.101 - 103. 〈10.1049/el.2017.2036〉. 〈hal-01767442〉

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