@article{abdullah_machine_2018,
	title = {Machine {Learning} {Algorithm} for {Wireless} {Indoor} {Localization}},
	url = {https://www.intechopen.com/books/machine-learning-advanced-techniques-and-emerging-applications/machine-learning-algorithm-for-wireless-indoor-localization},
	doi = {10.5772/intechopen.74754},
	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 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.},
	language = {en},
	urldate = {2020-03-30},
	journal = {Machine Learning - Advanced Techniques and Emerging Applications},
	author = {Abdullah, Osamah Ali and Abdel-Qader, Ikhlas},
	month = sep,
	year = {2018},
	note = {Publisher: IntechOpen}
}

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