  Visible light positioning has the potential to yield sub-centimeter accuracy
in indoor environments, yet conventional received signal strength (RSS)-based
localization algorithms cannot achieve this because their performance degrades
from optical multipath reflection. However, this part of the optical received
signal is deterministic due to the often static and predictable nature of the
optical wireless channel. In this paper, the performance of optical channel
impulse response (OCIR)-based localization is studied using an artificial
neural network (ANN) to map embedded features of the OCIR to the user
equipment's location. Numerical results show that OCIR-based localization
outperforms conventional RSS techniques by two orders of magnitude using only
two photodetectors as anchor points. The ANN technique can take advantage of
multipath features in a wide range of scenarios, from using only the DC value
to relying on high-resolution time sampling that can result in sub-centimeter
accuracy.
