Abstract
Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect.
Highlights
With the rapid development in the areas of mobile computing terminals and wireless techniques, indoor positioning systems have become unprecedentedly popular in recent years
Several indoor positioning systems have been proposed in recent years, which are based on infrared [2], ultrasound and Radio Frequency (RF) [3], etc
Because the RF-based indoor positioning systems are capable of providing a wide range of coverage and using the existed WLANs as the fundamental infrastructure, fingerprinting methods [4,5,6] based on WLANs, as one of the most popular RF techniques, outperforms the other existing indoor positioning systems in civilian fields [7,8]
Summary
With the rapid development in the areas of mobile computing terminals and wireless techniques, indoor positioning systems have become unprecedentedly popular in recent years. Sample points of certain areas such as confidential rooms within the radio map might be required to be clustered together, thereby providing the indoor positioning services of the dedicated area only to those authorized people In this case, the traditional methods may not run well. The proposed One versus One GA-SVM (OG-SVM) algorithm combined with the SDC method can reasonably cluster the radio map on the basis of coordinates and classify the RSS sample into sub- regions for coarse positioning. For another thing, we propose the Kernel PCA feature extraction algorithm based on Principal.
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