A Multi-Hypothesis Particle Filter for Indoor Dynamic Localization

Particle filters are frequently used to track mobile targets in indoor environments. However, standard particle filters encounter problems tracking targets facing decisions involving divergent choices such as intersections of corridors. The target either turns to the right or left, intermediate values are not possible. The available observations might not be (at least initially) sufficient to decide which choice was taken by the target. If the prediction model takes the wrong decision, the model will diverge very quickly from the real target location. In this paper we present a modified particle filter which tracks multiple hypotheses about the decisions made by the target. Whenever the target faces a decision, the particle cloud is split by a predefined, possibly probabilistic, hypothesis modifier. The resulting particle clouds have their own prediction model but they share the weight update and resampling step. This separation lasts until the observations can conclusively identify one of the hypotheses as the correct one, or until the hypotheses converge. Our approach uses measurement of wireless media signal strengths to provide the input necessary for the localization using the GRAIL system. We validate our model through experiments covering several movement and decision scenarios typical in indoor environments.