Open Space Indoor Navigation with RSS Sequence Transduction Neural Networks

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dc.contributor.author Al Ghareeb, Farhan
dc.date.accessioned 2022-04-26T08:41:30Z
dc.date.available 2022-04-26T08:41:30Z
dc.date.copyright 2020 en_US
dc.date.issued 2020-06-18
dc.identifier.uri http://hdl.handle.net/10725/13508
dc.description.abstract In conjunction to the extensive research conducted in outdoor positioning and navigation systems, indoor localization and navigation techniques have been gaining interest and attention with various technologies proposed in an attempt to achieve high positioning accuracy. Out of many proposed techniques, the received signal strength (RSS) fingerprinting-based approach remains widely adopted for indoor localization. One of the key challenges facing this approach is navigating an open space indoor location where the signal strength may vary indifferently from one position to the subsequent one. In this work, we propose a transduction neural network approach that takes as an input a sequence of past RSS fingerprints and accordingly predicts the current location, navigates the movement, and even predicts the future positions. The proposed models are based on Gated-Recurrent Unit (GRU) and Long-Short Term Memory (LSTM) recurrent neural networks (RNN). Moreover, we compare the accuracy of the proposed transduction neural networks to a non-recurrent neural network, then we evaluate the performance of all suggested models in a realistic environment with only WLAN RSS fingerprints, Cellular RSS Fingerprints, and both WLAN/Cellular RSS fingerprints. Our experimental results show that the LSTM-based architecture achieves accuracy of about one meter in an open area of 130m2 while only using Wi-Fi fingerprints. en_US
dc.language.iso en en_US
dc.subject Artificial satellites in navigation en_US
dc.subject Indoor positioning systems (Wireless localization) en_US
dc.subject Signal processing en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Open Space Indoor Navigation with RSS Sequence Transduction Neural Networks en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Engineering en_US
dc.author.school SOE en_US
dc.author.idnumber 200603406 en_US
dc.author.commembers Nakad, Zahi
dc.author.commembers Fawaz, Wissam
dc.author.department Electrical And Computer Engineering en_US
dc.description.physdesc 1 online resource (xi, 67 leaves) : col. ill. en_US
dc.author.advisor Saab, Samer
dc.keywords Neural Networks en_US
dc.keywords Recurrent Neural Networks en_US
dc.keywords Transduction Neural Networks en_US
dc.keywords Indoor Navigation en_US
dc.keywords Localization en_US
dc.keywords Prediction en_US
dc.keywords Received Signal Strength (RSS) en_US
dc.keywords RSS Fingerprinting en_US
dc.keywords Long-Short Term Memory (LSTM) en_US
dc.keywords Gated Recurrent Unit (GRU) en_US
dc.description.bibliographiccitations Includes bibliographical references (leaf 65-67) en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.295
dc.author.email farhan.alghareeb@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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