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The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that can be resource-consuming and cumbersome.
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A. Ahmed, A. Cosse, and L. Demanet, “A convex approach to blind deconvolution with diverse inputs,” in 2015 IEEE 6 th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) , Dec. 2015, pp. 5–8
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V. Cambareri and L. Jacques, “Through the Haze: A Non-Convex Approach to Blind Calibration for Linear Random Sensing Models,” 2016, in preparation
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