Fast localization of avalanche victims using sum of Gaussians

The probability of finding alive a person buried by a snow avalanche decreases dramatically with time. The best chance for the victims is to carry an avalanche beacon or ARVA (from the french: Appareilde Recherche de Victimes d'Avalanche), that transmits a magnetic field that can be detected by the rescuer's ARVA. However, the signals received are difficult to interpret and require people with good training on the actual searching techniques. In this paper we propose to address the search for victims as a SLAM problem: tracking the rescuer location while building a map of the locations of the victims under the snow, using 3D measurements of the magnetic field produced by their ARVAs. Given the high non-linearity of the problem, we propose a technique based on sum of Gaussians (SOGs) and extended Kalman filter. We present preliminary simulation results showing that this technique is faster and more accurate than the classical ones