  In the domain of RIS-based indoor localization, our work introduces two
distinct approaches to address real-world challenges. The first method is based
on deep learning, employing a Long Short-Term Memory (LSTM) network. The
second, a novel LSTM-PSO hybrid, strategically takes advantage of deep learning
and optimization techniques. Our simulations encompass practical scenarios,
including variations in RIS placement and the intricate dynamics of multipath
effects, all in Non-Line-of-Sight conditions. Our methods can achieve very high
reliability, obtaining centimeter-level accuracy for the 98th percentile (worst
case) in a different set of conditions, including the presence of the multipath
effect. Furthermore, our hybrid approach showcases remarkable resolution,
achieving sub-millimeter-level accuracy in numerous scenarios.
