Abstract
In this paper we consider the problem of estimation of scalar field distribution (e.g. pollutant, moisture, temperature) from noisy measurements collected by unmanned autonomous vehicles such as UAVs. The field is modelled as a sum of Fourier components/modes, where the number of modes retained and estimated determines in a natural way the approximation quality. An algorithm for estimating the modes using an online optimisation approach is presented, under the assumption that the noisy measurements are quantized. The algorithm can also be used to estimate time-varying fields. Simulation studies demonstrate the effectiveness of the proposed approach.
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