University of Twente Student Theses


LSTM-based Indoor Localization with Transfer Learning

Brattinga, M. (2022) LSTM-based Indoor Localization with Transfer Learning.

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Abstract:Localization techniques are the basis for applications such as pedestrian navigation, warehouse asset tracking, and augmented reality. Indoor localization techniques based on the Received Signal Strength Indicator (RSSI) exist that take advantage of existing infrastructure, such as WiFi routers and smartphones, present in practically ev- ery building in our modern society. To overcome the chal- lenges caused by the attenuation and scattering of wire- less signals in indoor environments, machine learning ap- proaches to improve fingerprinting localization have been studied. Recurrent Neural Networks (RNNs), and in par- ticular Long Short-TermMemory (LSTM), have been found to be effective for indoor localization. Deploying finger- printing localization with machine learning, however, is expensive. As every environment has different character- istics, a vast amount of data has to be collected for ev- ery new environment to train the model on, in order to obtain adequate accuracy. Transfer Learning (TL) tech- niques have been developed to reduce the amount of re- quired training data for RNNs, lowering deployment costs, however this has not been a topic of research in LSTM- based indoor localization yet. This paper proposes an LSTM-based fingerprinting localization architecture, that utilizes Transfer Learning techniques to provide high ac- curacy and little deployment costs. This makes indoor localization cheaper and easier to use, enabling it to be- come more broadly available. A prototype of the proposed model has been made to evaluate the accuracy and deploy- ment costs. The proposed TL techniques significantly im- prove LSTM-based fingerprinting and reduce deployment costs for indoor localization.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
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