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Particle filters for positioning in wireless networks

Positioning of road vehicles in wireless radio networks is a highly non-linear multi-sensor problem. The radio measurements deliver snapshot information of position as circle, radial and hyperbolic lines with various reliability, and possibly also complex power attenuation maps. Further, fading and absence of line of sight give complicated disturbances on these measurements. Temporal and spatial prior knowledge may include maximal velocity and acceleration of the mobile unit, and for automotive applications also the constraint that most of the time is spent on roads, which are stored in a digital map. We outline a framework where all this information can be incorporated, and the true a posteriori distribution of position can be approximated with arbitrary accuracy to be traded off with real-time requirements. The algorithm is based on the particle filter, and we demonstrate it on a few simulation examples.