Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning

In this work, the Monte Carlo algorithm to determine the variance on the distance estimation in Received Signal Strength-based visible light positioning is considered. The method is build on the maximization of the signal-to-noise-ratio by means of matched filtering, and leads to a number of characteristics that are typically only obtained after intensive analytical elaborations. It is shown that the results match those obtained by calculating the Cramer-Rao lower bound when only the noise is considered as non-deterministic. It is demonstrated that the method is also applicable when multiple physical parameters exhibit a probability distribution, leading to an assessment of the distance estimation accuracy in more realistic settings.