  This study describes a UWB and Machine Learning (ML)-based indoor positioning
system. We propose a simple mathematical strategy to create data to reduce the
job of measurements for fingerprint-based indoor localization systems. A
considerable number of measurements can be avoided this way. The paper compares
and contrasts the performance of four distinct models. Most test locations'
average error may be reduced to less than 150 mm using the best model.
