Through the Haze: A Non-Convex Approach to Blind Calibration for Linear Random Sensing Models

Abstract

Computational sensing strategies often suffer from calibration errors in the physical implementation of their ideal sensing models. Such uncertainties are typically addressed by using multiple, accurately chosen training signals to recover the missing information on the sensing model, an approach that can be resource-consuming and cumbersome. Conversely… (More)

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