Fingerprinting-based Visible Light Positioning is a promising candidate to perform large-scale indoor positioning tasks. In fingerprinting, signal characteristics are grouped in a fingerprint map together with the respective locations inside the indoor environment. By comparing live signal measurements with the fingerprint map, the closest match is selected as the current position estimate. However, the fingerprint map has to be generated beforehand in the so-called offline phase, which is the time-consuming process of sampling the environment, in which the positioning task is desired, for signal characteristics. Here, we propose a fingerprint-based positioning approach for which we mitigate the need for the offline phase by taking advantage of the VLC data transmission capabilities of the LED luminaires of the obligatory room lighting. Based on the transmitted data on room and luminaire configurations to the receiving device, the illumination characteristics in the room can be calculated by simplified analytical formalisms, substituting the need for an experimentally measured offline phase. We demonstrate the effectiveness of our approach with the help of ray-tracing simulations and under the assumption that the receiving device is equipped with an angular sectored receiver. The results of the ray-tracing simulations mimic real world measurements with the receiver in the online phase. We show that decimeter level accuracies down to centimeter level accuracies are achievable for such an approach.
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