research-article

Localization of mobile users using trajectory matching

Authors:

Branislav Kusy,

Leonidas GuibasAuthors Info & Claims

MELT '08: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments

Pages 123 - 128

https://doi.org/10.1145/1410012.1410041

Published: 19 September 2008 Publication History

Abstract

We present an algorithm enabling localization of moving wireless devices in an indoor setting. The method uses only RF signal strength and can be implemented without specialized hardware. The mobility of the users is modeled by learning a function mapping a short history of signal strength values to a 2D position. We use radial basis function (RBF) fitting to learn a reliable estimate of a mobile node's position given its past signal strength measurements.

Even though we deal with extremely noisy measurements in a cluttered indoor setting, nodes are not required to be stationary during measurement or learning. We evaluate our algorithm in a real indoor setting using MicaZ motes, achieving an average localization accuracy of 1.3 m. In our experiments, using historical data improves the localization accuracy by almost a factor of two compared to using only the most current measurements.

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Cited By

Gidey HGuo XLi LZhang Y(2022)Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature SelectionSensors10.3390/s2215584022:15(5840)Online publication date: 4-Aug-2022
https://doi.org/10.3390/s22155840
Chen GGuo XLiu KLi XYang J(2022)RWKNN: A Modified WKNN Algorithm Specific for the Indoor Localization ProblemIEEE Sensors Journal10.1109/JSEN.2022.315590222:7(7258-7266)Online publication date: 1-Apr-2022
https://doi.org/10.1109/JSEN.2022.3155902
Guo RBlu T(2021)Exploring the Geometry of One-Dimensional SignalsIEEE Transactions on Signal Processing10.1109/TSP.2021.311291469(5299-5312)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TSP.2021.3112914
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Index Terms

Localization of mobile users using trajectory matching
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

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Published In

cover image ACM Conferences

MELT '08: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments

September 2008

142 pages

ISBN:9781605581897

DOI:10.1145/1410012

Program Chairs:
Ying Zhang
Palo Alto Research Center, USA
,
Yinyu Ye
Stanford University, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 19 September 2008

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MobiCom08

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MobiCom08: Annual International Conference on Mobile Computing and Networking

September 19, 2008

California, San Francisco, USA

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Citations

Cited By

Gidey HGuo XLi LZhang Y(2022)Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature SelectionSensors10.3390/s2215584022:15(5840)Online publication date: 4-Aug-2022
https://doi.org/10.3390/s22155840
Chen GGuo XLiu KLi XYang J(2022)RWKNN: A Modified WKNN Algorithm Specific for the Indoor Localization ProblemIEEE Sensors Journal10.1109/JSEN.2022.315590222:7(7258-7266)Online publication date: 1-Apr-2022
https://doi.org/10.1109/JSEN.2022.3155902
Guo RBlu T(2021)Exploring the Geometry of One-Dimensional SignalsIEEE Transactions on Signal Processing10.1109/TSP.2021.311291469(5299-5312)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TSP.2021.3112914
Zhang MJia JChen JYang LGuo LWang X(2021)Real-time indoor localization using smartphone magnetic with LSTM networksNeural Computing and Applications10.1007/s00521-021-05774-5Online publication date: 27-Feb-2021
https://doi.org/10.1007/s00521-021-05774-5
Guo RBlu T(2020)FRI Sensing: Retrieving the Trajectory of a Mobile Sensor From Its Temporal SamplesIEEE Transactions on Signal Processing10.1109/TSP.2020.302281668(5533-5545)Online publication date: 2020
https://doi.org/10.1109/TSP.2020.3022816
Bai SYan MWan QHe LWang XLi J(2020)DL-RNN: An Accurate Indoor Localization Method via Double RNNsIEEE Sensors Journal10.1109/JSEN.2019.293641220:1(286-295)Online publication date: 1-Jan-2020
https://doi.org/10.1109/JSEN.2019.2936412
Yan MXu FBai SWan Q(2018)A Noise Reduction Fingerprint Feature for Indoor Localization2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)10.1109/WCSP.2018.8555638(1-6)Online publication date: Oct-2018
https://doi.org/10.1109/WCSP.2018.8555638
Bai SYan MLuo YWan Q(2018)RFedRNN: An End-to-End Recurrent Neural Network for Radio Frequency Path FingerprintingRecent Trends and Future Technology in Applied Intelligence10.1007/978-3-319-92058-0_54(560-571)Online publication date: 30-May-2018
https://doi.org/10.1007/978-3-319-92058-0_54
Liu ZCai QWang SXu XDou WYu S(2018)A Cloud Service Enhanced Method Supporting Context-Aware ApplicationsMobile Networks and Management10.1007/978-3-319-90775-8_22(277-290)Online publication date: 9-May-2018
https://doi.org/10.1007/978-3-319-90775-8_22
Das AChaki R(2017)Localization based Anti-Void Clustering Approach (LAVCA) for Energy Efficient Routing in Wireless Sensor NetworkComputer Information Systems and Industrial Management10.1007/978-3-319-59105-6_25(290-302)Online publication date: 17-May-2017
https://doi.org/10.1007/978-3-319-59105-6_25
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