A Spatial Division Clustering Method and Low Dimensional Feature Extraction Technique Based Indoor Positioning System

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.

Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System

“…In terms of the SVM method (optimized by genetic algorithm) [10,11] and the proposed RF method, their subregions are able to be defined freely. Therefore the number of RPs within a region is set according to the corners or partitions of the building and then assigned to both methods.…”

“…Moreover, the coarse locating scheme can also work on three-dimensional scenario for discriminating different floors or as the basis for positioning on a size-reduced radio map. In addition, compared with some typical machine learning techniques, such as artificial neural network (ANN) [8] and support vector machine (SVM) [9][10][11], the proposed RF based coarse positioning method shows a better performance in terms of classification accuracy and training time complexity.…”

Multiregional secure localization using compressive sensing in wireless sensor networks

“…This model can optimize redundant dictionary arrangements with excellent performance for multiple source localization. In , a sampling database was generated in an offline phase by collecting signals from individual network grids. A clustering algorithm was then used to group all anchors and construct a redundant dictionary.…”

Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network

“…To solve the "curse of dimensionality" problem, existing methods can be generally categorized into two classes: AP selection [14] and feature extraction [15]. In the former, several criterions, including Fisher criterion [16] and mutual information gain criterion [17], have been proposed to select the most important APs.…”