NDLI: Gaussian processes for learning-based indoor localization

Content Provider	IEEE Xplore Digital Library
Author	Bekkali, A. Masuo, T. Tominaga, T. Nakamoto, N. Ban, H.
Copyright Year	2011
Description	Author affiliation: Advanced Telecommunications Research Institute (ATR), Kyoto, Japan (Nakamoto, N.; Ban, H.) \|\| NTT Energy and Enviroment Systems Laboratories Tokyo, Japan (Bekkali, A.; Masuo, T.; Tominaga, T.)
Abstract	The development of an efficient and accurate location sensing systems for indoor environments, based on the received signal strength (RSS) data, is usually a challenging task. In this paper, we discuss the feasibility of using Gaussian Processes (GPs) regression for learning based indoor localization algorithm. The GP is one of the machine learning algorithms that can be used to model a complete RSS map from few training data. We investigate the use of three different covariance functions, i.e. Squared Exponential (SE), Matérn, and Rational Quadratic (RQ), to find the suitable one for the indoor localization, and then compare their performance to the traditional weighted k-Nearest Neighbors (k-NN) algorithm. We show that GP regression can significantly outperform the k-NN, while keeping the training cost at a reasonable level. Furthermore, although, the smoothness property of SE covariance function, we demonstrate that GP-SE covariance provides better accuracy compared to GP-Matérn and GP-RQ, particularly, when a few training data are available.
Starting Page	1
Ending Page	6
File Size	1378034
Page Count	6
File Format	PDF
ISBN	9781457708930
e-ISBN	9781457708947
DOI	10.1109/ICSPCC.2011.6061737
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Maximum likelihood estimation Training data Indoor environments Gaussian processes Data models Calibration
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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