Definition of a Neural Network for an IR Positioning System based on Energy Measurements

Infrared Local Positioning Systems (IRLPS) offer a cost-effective and accurate alternative for indoor localization, where GNSS signals are typically not available. Despite their potential, IRLPS face significant challenges such as noise, multipath effects, multiple access interference, and calibration requirements, which limit their performance. In response, this work explores the integration of machine learning by proposing a Feedforward Neural Network (FNN) trained on energy measurements collected from a quadrant photodiode. We conducted a thorough analysis of hyperparameters and an ablation study across five neural network topologies and identified configurations that balance accuracy and model complexity. Experimental evaluations in a controlled indoor environment (2.4×2.4×3.4 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) demonstrate that even simple FNN architectures can generalize well and achieve a high accuracy, with 90% of the positioning errors being below 0.05 m.