RFID - Hybrid Scene Analysis-Neural Network system for 3D Indoor Positioning optimal system arrangement approach

The purpose of this research is to find an optimal number and configuration of readers in RFID based 3D Indoor Positioning System. The system applies a Hybrid Scene Analysis - Neural Network algorithm to estimate target's position with a desired accuracy. The system's accuracy and cost depend on a number of utilized readers and their arrangement. Readers' deployment is crucial for the localization accuracy too. The system optimization enhances the system cost-efficiency. The arrangement analysis was based on simulations and validated by physical experiment. The results of this research define a tradeoff between a number of readers and their deployment and the system performance in terms of localization accuracy.

[1]  Dursun Delen,et al.  An RFID network design methodology for asset tracking in healthcare , 2010, Decis. Support Syst..

[2]  SungHee Jeon,et al.  A RFID Reader Configuration with an Enhanced Recognition Property for Indoor Positioning , 2009, 2009 Fifth International Joint Conference on INC, IMS and IDC.

[3]  Antonio Pietrabissa,et al.  Optimal planning of sensor networks for asset tracking in hospital environments , 2013, Decis. Support Syst..

[4]  Wlodek Kulesza,et al.  RFID Multi-target Tracking Using the Probability Hypothesis Density Algorithm for a Health Care Application , 2011, IT Revolutions.

[5]  R. Stolkin,et al.  Probabilistic analysis of a passive acoustic diver detection system for optimal sensor placement and extensions to localization and tracking , 2007, OCEANS 2007.

[6]  Zhu Zhu,et al.  A RFID-based Intelligent Warehouse Management System Design and Implementation , 2011, 2011 IEEE 8th International Conference on e-Business Engineering.

[7]  Hakima Chaouchi,et al.  WIFE: Wireless Indoor Positioning Based on Fingerprint Evaluation , 2009, Networking.

[8]  Chanakya Kumar,et al.  Comparison between Innovative Approaches of RFID Based Localization Using Fingerprinting Techniques for Outdoor and Indoor Environments , 2013 .

[9]  Yunlong Zhu,et al.  RFID networks planning using a multi-swarm optimizer , 2009, 2009 Chinese Control and Decision Conference.

[10]  A.R. Al-Ali,et al.  Mobile RFID Tracking System , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[11]  Dingwei Wang,et al.  Review on modeling and optimization problems about RFID technology and applications , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).

[12]  Li Lin,et al.  3D Goods allocation in warehouse with L-GEM based 3-D RFID positioning , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[13]  Meie Shen,et al.  Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination , 2012, IEEE Transactions on Industrial Informatics.

[14]  Wlodek Kulesza,et al.  Performance analysis of an RFID-based 3D indoor positioning system combining scene analysis and neural network methods , 2013 .

[15]  Jiandan Chen,et al.  Planning of a Multi Stereo Visual Sensor System - Depth Accuracy and Variable Baseline Approach , 2007, 2007 3DTV Conference.

[16]  Quanyi Ge,et al.  RFID Emergency System for Tumble Detection of Solitary People , 2012 .