Journal ArticleDOI
Multilayer ANN indoor location system with area division in WLAN environment
Reads0
Chats0
About:
This article is published in Journal of Systems Engineering and Electronics.The article was published on 2010-01-03. It has received 30 citations till now. The article focuses on the topics: Division (mathematics).read more
Citations
More filters
Journal ArticleDOI
Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors
TL;DR: Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone, while it incurs much less computational complexity.
Journal ArticleDOI
5 G WiFi Signal-Based Indoor Localization System Using Cluster k-Nearest Neighbor Algorithm:
TL;DR: The proposed cluster k-nearest neighbor (KNN) algorithm with 5 G WiFi signal to reduce the environmental interference and improve the localization performance without additional equipment is proposed and implemented and the performance with existent popular algorithms is evaluated.
Journal ArticleDOI
Fingerprint indoor positioning algorithm based on affinity propagation clustering
TL;DR: Experimental results show that the proposed algorithm will significantly improve the performance of the probability distribution-aided positioning algorithm in both the positioning accuracy and real-time ability.
Journal ArticleDOI
SCaNME: Location tracking system in large-scale campus Wi-Fi environment using unlabeled mobility map
TL;DR: A novel location tracking system called SCaNME (Shotgun Clustering-aided Navigation in Mobile Environment) which iteratively sequences the clusters of sporadically recorded received signal strength measurements and adaptively construct a mobility map of the environment for location tracking is proposed.
Journal ArticleDOI
Indoor Positioning of RBF Neural Network Based on Improved Fast Clustering Algorithm Combined With LM Algorithm
TL;DR: An indoor positioning algorithm based on an improved fast clustering algorithm combined with a Levenberg–Marquardt (LM) algorithm is proposed and the results data confirm the effectiveness and applicability of the proposed algorithm.