The Application of Discrete Fourier Transforms in Reducing the Calibration Effort of Indoor Positioning System

For the past decade, Wi-Fi signal fingerprinting algorithm has been considered as a prevalent solution
for indoor positioning systems. Fingerprinting based methods require a massive database of Wi-Fi
signal samples to calibrate the indoor positioning system and to achieve a high location accuracy.
Traditionally the calibration procedure requires human intervention and is very time-consuming, which
makes a large-scale deployments of indoor positioning systems non-trivial. Objective of this research to
minimise the manual workload by combining the conventional sampling algorithm with signal
prediction. In contrast to traditional algorithms, proposed method requires only few signal samples to
be collected and rest of the data are approximated using Discrete Fourier Transforms. The main
objective of our research is to reduce the calibration effort while maintaining an acceptable location
accuracy of the indoor positioning systems.