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