Avalanche victim search via robust observers

This paper introduces a new approach for victim localization in avalanches that will be exploited by UAVs using the ARVA sensor. We show that the nominal ARVA measurement can be linearly related to a quantity that is sufficient to reconstruct the victim position. We explicitly deal with a robust scenario in which the measurement is actually perturbed by a noise that grows with the distance to the victim and we propose an adaptive control scheme made of a least-square identifier and a trajectory generator whose role is both to guarantee the persistence of excitation for the identifier and to steer the ARVA receiver towards the victim. We show that the system succeeds in localizing the victim in a domain where the ARVA output is sufficiently informative and illustrate its performance in simulation. This new approach could significantly reduce the searching time by providing an exploitable estimate before having reached the victim. The work is framed within the EU project AirBorne whose goals is to develop at TRL8 a drone for quick localization of victims in avalanche scenarios.