Effects of unknown shadowing and non-line-of-sight on indoor tracking using visible light

Indoor target tracking has garnered interest as communication systems and mobile device capabilities advance. Visible light communication (VLC) is an alternative to RF methods that uses light emitting diodes. In this paper, probabilistic filtering algorithms (particle and extended Kalman filters) are used for indoor tracking. The performance of the filters is compared with an another positioning method: trilateration. Probabilistic filtering methods are shown to be more reliable than trilateration for indoor tracking when non-line-of-sight components are significant. The probabilistic filtering algorithms require a light intensity map that is collected as a fingerprint map prior to tracking. The effect of unpredictable shadowing as the conditions change is examined in a scenario where one of the lamps has been shadowed. Our algorithm tracks the user equipment with less error than trilateration. The results also show that the tracking accuracy for our algorithms is on the order of the grid resolution of the fingerprint map for high signal-to-noise ratio environments.