Abstract:
A mean-offset classification technique was identified. It was found that the meanoffset
classifier provides stability under dynamic indoor conditions and provides consistent results
when training and test data combinations are swept from 10 – 95%. In this paper the meanoffset
classifier is compared to the K-Nearest Neighbors (KNN) and Naïve Bayesian (NB) classifiers,
with a view of developing an adaptable and computationally efficient indoor localization
model using machine learning principles. It was seen that the mean-offset classifier improved
results considerably and achieved an accuracy of 0.85 m and 1.15 m under line-of-sight (LOS)
and non-line-of-sight (NLOS) conditions in residential areas.
