| Summary: | Indoor localization based on visible light and visible light communication has become a viable alternative to radio frequency wireless-based techniques. Modern visible light position (VLP) systems have been able to attain sub-decimeter level accuracy within standard room environments. However, a major limitation is their reliance on line-of-sight visibility between the tracked object and the lighting infrastructure. This paper introduces fused application of light-based positioning coupled with onboard network localization (Falcon), a VLP system, which incorporates convolutional neural network-based wireless localization to remove this limitation. This system has been tested in real-life scenarios that cause traditional VLP systems to lose accuracy. In a hallway with luminaires along one axis, the Falcon managed to attain position estimates with a mean error of 0.09 m. In a large room where only a few luminaires were visible or the receiver was completely occluded, the mean error was 0.12 m. With the luminaires switched off, the Falcon managed to correctly classify the target 99.59% of the time to within a 0.9-m<sup>2</sup> cell and with 100% accuracy within a 1.6-m<sup>2</sup> cell in the room and hallway, respectively.
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