Machine Learning Algorithm for Wireless Indoor Localization
Author | : Osamah Ali Abdullah |
Publisher | : |
Total Pages | : |
Release | : 2018 |
ISBN-10 | : OCLC:1154242005 |
ISBN-13 | : |
Rating | : 4/5 (05 Downloads) |
Download or read book Machine Learning Algorithm for Wireless Indoor Localization written by Osamah Ali Abdullah and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates 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 probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m.