Adaptive Reconstruction Along Mobile Sensing Paths

We address the problem of reconstructing missing parts of mobile sensing signals. Such a case occurs when sensor information is occluded or not transmitted over finite periods of time. As the sampling rate along each path is essentially unlimited, we consider the asymptotic case of having continuous-time information from the sensor. We embed the partially available signal in a functional space of smooth and finite-energy functions, while adapting the parameters of the space to the signal at hand. We then analytically solve a specifically designed error measure and obtain a minimum-norm reconstruction for the missing parts. We demonstrate the proposed algorithm for both simulated- and real data.