EFFICIENT DATA ASSIMILATION METHOD FOR THE CURLING STONE USING PARTICLE FILTER

Abstract

Computation cost of particle filter, which is a representative method for data assimilation method, is generally large. Some problems in real world require real-time prediction by data assimilation. In this paper, we investigate a method to reduce the computation cost of particle filter for the prediction of curling stone trajectories for rapid data assimilation before and during a game. In order to reduce the number of parameters for data assimilation, trajectory angle and y-coordinate are simply calculated by a least square method. In advance 100,000 trajectory scenarios are calculated and scenarios which stop around the location where the stone stop actually in measured data. The likelihood is calculated only for the extracted scenarios. By these procedures, the computation time could be successfully reduced to less than 1 second.