Analysis of WiFi localization techniques for kidnapped robot problem

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dc.contributor.authorPEGORELLI NETO, A.
dc.contributor.authorFlavio Tonidandel
dc.contributor.authorOrcidhttps://orcid.org/0000-0003-0345-668X
dc.date.accessioned2022-08-01T06:02:50Z
dc.date.available2022-08-01T06:02:50Z
dc.date.issued2022-04-05
dc.description.abstract© 2022 IEEE.This work proposes an analysis of the earliest indoor localization techniques based on recurrent neural networks (RNN) like Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM), including k-Nearest Neighbors (KNN) machine learning, to process WiFi received signal strength data (RSS) for the kidnapped robot problem (KRP). The proposed solutions uses processed data generated in a Webots simulation of the iRobot Create robot, with the RSS signals simulated based on fingerprinting data from a real indoor area with 6 dedicated access points as reference. The efficiency of each system is evaluated using cumulative distribution function for several access point combinations, noise and vanishing levels for a model trained with the base test parameters from the reference material, with all 6 access points (APs) activated, ldBm Gaussian noise, 10% masking level and using 10 time steps of data as history inputs. The results show that RNN systems can achieve mean localization accuracy between $0.44\mathrm{m}\pm 0.39\mathrm{m}$ for LSTM and $0.50\mathrm{m}\pm 0.38\mathrm{m}$ for GRU and the KNN proposal reaching $0.68\mathrm{m}\pm 0.73\mathrm{m}$, proving the capability of those systems to recover from a KRP event keeping similar results obtained without any event.
dc.description.firstpage53
dc.description.lastpage58
dc.identifier.citationPEGORELLI NETO, A.; TONIDANDEL, F. Analysis of WiFi localization techniques for kidnapped robot problem. 2022 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022, April, 2022.
dc.identifier.doi10.1109/ICARSC55462.2022.9784792
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4542
dc.relation.ispartof2022 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022
dc.rightsAcesso Restrito
dc.subject.otherlanguageGated Recurrent Unit
dc.subject.otherlanguagek-Nearest Neighbor
dc.subject.otherlanguageKidnapped Robot Problem
dc.subject.otherlanguageLong-Short Term Memory
dc.subject.otherlanguageReceived Signal Strength Indicator
dc.subject.otherlanguageRecurrent Neural Networks
dc.subject.otherlanguageWiFi Localization
dc.titleAnalysis of WiFi localization techniques for kidnapped robot problem
dc.typeArtigo de evento
fei.scopus.citations1
fei.scopus.eid2-s2.0-85133019461
fei.scopus.subjectAccess points
fei.scopus.subjectGated recurrent unit
fei.scopus.subjectIndoor localization techniques
fei.scopus.subjectKidnapped robot problems
fei.scopus.subjectLocalization technique
fei.scopus.subjectMachine-learning
fei.scopus.subjectNetwork likes
fei.scopus.subjectReceived signal strength indicators
fei.scopus.subjectWi-Fi localizations
fei.scopus.subjectWifi received signal strengths
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85133019461&origin=inward
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