Improvement of the Interpolation for the Self Localization Technique “CEPHEID” Using Neural Network with Attention Mechanism

This paper delves into the indoor positioning methodology developed by the authors, denoted as “CEPHEID.” CEPHEID achieves self-localization by discerning the distinct flickering pattern exhibited by each individual lighting fixture, patterns imperceptible to human observers. The original CEPHEID fundamentally operates as a classifier model, inherently constraining the positional estimation resolution to the placement of these lighting fixtures. To surmount this limitation and enhances its positioning accuracy, the authors introduce a novel interpolation technique, leveraging a variant of multiple head attention mechanism. This techniques is designed to facilitate the integration of information from sources, thereby improving the overall performance of CEPHEID in the context of indoor positioning. To access the efficacy of this methodology, an indoor positioning experiment is conducted, wherein the performance of the proposed technique is evaluated.