1. Gaussian Process Regression Plus Method for Localization Reliability Improvement
- Author
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Chung-Ming Own, Kehan Liu, and Zhaopeng Meng
- Subjects
Computer science ,Gaussian Process Regression ,02 engineering and technology ,computer.software_genre ,Biochemistry ,Article ,Analytical Chemistry ,Set (abstract data type) ,Naive Bayes ,Naive Bayes classifier ,symbols.namesake ,Kriging ,RSS fingerprinting ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Instrumentation ,Gaussian process ,Reliability (statistics) ,Covariance matrix ,020206 networking & telecommunications ,Atomic and Molecular Physics, and Optics ,location estimation ,symbols ,020201 artificial intelligence & image processing ,Data mining ,computer - Abstract
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.
- Published
- 2016