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Méthodes informées de factorisation matricielle pour l'étalonnage de réseaux de capteurs mobiles et la cartographie de champs de pollution

Clément Dorffer 1
1 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Mobile crowdsensing aims to acquire geolocated and timestamped data from a crowd of sensors (from or connected to smartphones). In this thesis, we focus on processing data from environmental mobile crowsensing. In particular, we propose to revisit blind sensor calibration as an informed matrix factorization problem with missing entries, where factor matrices respectively contain the calibration model which is a function of the observed physical phenomenon (we focus on approaches for affine or nonlinear sensor responses) and the calibration parameters of each sensor. Moreover, in the considered air quality monitoring application, we assume to pocee- some precise measurements---which are sparsely distributed in space and time---that we melt with multiple measurements from the mobile sensors. Our approaches are "informed" because (i) factor matrices are structured by the problem nature, (ii) the physical phenomenon can be decomposed using sparse decomposition with a known dictionary or can be approximated by a physical or a geostatistical model, and (iii) we know the mean calibration function of the sensors to be calibrated. The proposed approaches demonstrate better performances than the one based on the completion of the observed data matrix or the multi-hop calibration method from the literature, based on robust regression. Finally, the informed matrix factorization formalism also provides an accurate reconstruction of the observed physical field.
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Clément Dorffer. Méthodes informées de factorisation matricielle pour l'étalonnage de réseaux de capteurs mobiles et la cartographie de champs de pollution. Traitement du signal et de l'image [eess.SP]. Université du Littoral Côte d'Opale, 2017. Français. ⟨tel-02074686⟩

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