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Performance Comparison of Indoor Fingerprinting Techniques Based on Artificial Neural Network
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Publication Year
2018-07-02
Journal
IEEE Region 10 Annual International Conference, Proceedings/TENCON
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Region 10 Annual International Conference, Proceedings/TENCON, Vol.2018-October, pp.56-61
Keyword
fingerprinting localizationindoor navigationindoor positioningmultiple neural networkssingle neural network
Mesh Keyword
Fingerprinting localizationIndoor navigationIndoor positioningLocalisationMultiple neural networksNeural-networksPerformance comparisonReceived signal strength indicatorsSingle neuralSingle neural network
All Science Classification Codes (ASJC)
Computer Science ApplicationsElectrical and Electronic Engineering
Abstract
An indoor fingerprinting localization algorithm based on a single Artificial Neural Network (ANN) model may be subject to the Received Signal Strength Indicator (RSSI) fluctuation than multiple neural networks based fingerprinting algorithm. To date, there has not been an adequate analytical study that has investigated the performance comparison of both models. In this work, a multiple neural network fingerprinting localization model is proposed which predicts the estimated position using the result of the combination of several single neural networks. Additionally, a K-nearer coarse localizer is used to perform the position estimation task. This model has been evaluated by comparison with the existing K-Nearer Neighbor (KNN) based method and single neural network based fingerprinting localization method on a corridor and Office area and on publicly available RSSI database. The results of the proposed model are more accurate than those with KNN and single neural network model. We believe that the multiple neural networks model will be more robust for positioning algorithms using several types of features data at the same time.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36320
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063227409&origin=inward
DOI
https://doi.org/10.1109/tencon.2018.8650230
Journal URL
http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000751
Type
Conference
Funding
ACKNOWLEDGMENT This work was supported by the National Research Foundation (NRF) of South Korea funded by the Minister of Education, Science and Technology (under Grant 2017R1D1A1B03035229).
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Lee, Chaewoo이채우
Department of Electrical and Computer Engineering
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