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Blind mobile sensor calibration using an informed nonnegative matrix factorization with a relaxed rendezvous model

Abstract : In this paper, we consider the problem of blindly calibrating a mobile sensor network-i.e., determining the gain and the offset of each sensor-from heterogeneous observations on a defined spatial area over time. For that purpose, we previously proposed a blind sensor calibration method based on Weighted Informed Nonnegative Matrix Factorization with missing entries. It required a minimum number of rendezvous-i.e., data sensed by different sensors at almost the same time and place-which might be difficult to satisfy in practice. In this paper we relax the rendezvous requirement by using a sparse decomposition of the signal of interest with respect to a known dictionary. The calibration can thus be performed if sensors share some common support in the dictionary, and provides a consistent performance even if no sensors are in exact rendezvous.
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Clément Dorffer, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. Blind mobile sensor calibration using an informed nonnegative matrix factorization with a relaxed rendezvous model. 41st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016), Mar 2016, Shanghai, China. pp.2941-2945, ⟨10.1109/ICASSP.2016.7472216⟩. ⟨hal-01367338⟩

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