Machine Learning Algorithm for Wireless Indoor Localization

Abstract

Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorpo- rates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordi- nates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilis- tic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m