A calibration process for environmental sensor array supporting uncertainties and unknown perturbing factors: performances on simulated and real data

The presence of systematic interferents and high measurement uncertainties strongly diminishes performances of environmental sensor array when deployed in real environment. The present paper shows how to account for such perturbations in a Bayesian framework and demonstrates the increase in performances - up to 50% on simulated and 12% on experimental data.