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Sensors 2016, 16(8), 1193;

Gaussian Process Regression Plus Method for Localization Reliability Improvement

School of Computer Software, Tianjin University, Tianjin 300350, China
School of Computer Software, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
Author to whom correspondence should be addressed.
Academic Editors: Lyudmila Mihaylova, Byung-Gyu Kim and Debi Prosad Dogra
Received: 3 May 2016 / Revised: 7 July 2016 / Accepted: 14 July 2016 / Published: 29 July 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Location data are among the most widely used context data in context-aware and ubiquitous computing applications. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The most useful method is focused on the received signal strength and provides a set of signal transmission access points. However, compiling a manual measuring Received Signal Strength (RSS) fingerprint database involves high costs and thus is impractical in an online prediction environment. The system used in this study relied on the Gaussian process method, which is a nonparametric model that can be characterized completely by using the mean function and the covariance matrix. In addition, the Naive Bayes method was used to verify and simplify the computation of precise predictions. The authors conducted several experiments on simulated and real environments at Tianjin University. The experiments examined distinct data size, different kernels, and accuracy. The results showed that the proposed method not only can retain positioning accuracy but also can save computation time in location predictions. View Full-Text
Keywords: location estimation; RSS fingerprinting; Gaussian Process Regression; Naive Bayes location estimation; RSS fingerprinting; Gaussian Process Regression; Naive Bayes

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Liu, K.; Meng, Z.; Own, C.-M. Gaussian Process Regression Plus Method for Localization Reliability Improvement. Sensors 2016, 16, 1193.

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