Mean-offset classifier based on Wi-Fi indoor positioning system

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.