Accuracy improvement of RFID based 2D tracking and localisation

The purpose of localization and tracking technology in indoor application is to extract moving object parameters accurately and precisely. This thesis investigates the problem of how to utilize RFID technique for the accurate and precise extraction of indoor 2D moving object position parameters. Firstly, a framework named RFID-Loc with three modules: RFID-Loc Infrastructure, RFID-Loc Data Filter and RFID-Loc Localisation Algorithm, is established from a theoretical perspective. This framework can guide the research and design of methods used in an RFID based object localisation system with enhanced localisation accuracy and precision. Secondly, from practical perspective, few methods are proposed in RFID-Loc framework to improve the localisation accuracy and precision. A sparse RFID Tag Arrangement strategy is proposed in this RFID-Loc framework, aiming at reducing the impacts of regular false reading error from RFID infrastructure level on localisation precision. The efficiency of this methods and the assumptions upon which it relies, are investigated empirically. A rectangle-based feature selection method is justified as the major RFID Data Filter algorithm, with the capability of maximally reducing regular false reading errors. The possibility to resist unexpected false reading error in an RFID-Loc system is investigated by discussing and comparing several RFID-based localisation algorithms. A dynamic localisation algorithm for RFID-Loc system is proposed to accurately and precisely extract moving object position parameters overtime in an RFID-Loc system. This algorithm is shown to have a better capability of resisting unexpected false reading error than conventional localisation algorithms used in RFID-based localisation systems, while having a higher computational complexity. By following the theoretical guidelines in RFID-Loc framework and implementing the proposed methods, the experimental results demonstrate that the localisation accuracy and precision can be significantly improved, up to 10 centimetres and 3 centimetres under current RFID devices

[1]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[2]  Mun Leng Ng,et al.  The reader collision problem in RFID systems , 2005, 2005 IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications.

[3]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[4]  Ding Zhen-hua,et al.  A taxonomy model of RFID security threats , 2008, 2008 11th IEEE International Conference on Communication Technology.

[5]  Ari Juels,et al.  RFID security and privacy: a research survey , 2006, IEEE Journal on Selected Areas in Communications.

[6]  Craig W. Thompson,et al.  Architecting RFID Middleware , 2006, IEEE Internet Computing.

[7]  Hyuckjae Lee,et al.  Query tree-based reservation for efficient RFID tag anti-collision , 2007, IEEE Communications Letters.

[8]  Ron Weinstein,et al.  RFID: a technical overview and its application to the enterprise , 2005, IT Professional.

[9]  Jerry Lopez,et al.  A low-cost custom HF RFID system for hand washing compliance monitoring , 2009, 2009 IEEE 8th International Conference on ASIC.

[10]  Yoshihiko Kimuro,et al.  Self-localization of mobile robots with RFID system by using support vector machine , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[11]  Wolfram Burgard,et al.  Mapping and localization with RFID technology , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[12]  Mark Fiala,et al.  Comparing ARTag and ARToolkit Plus fiducial marker systems , 2005, IEEE International Workshop on Haptic Audio Visual Environments and their Applications.

[13]  Stefan B. Williams Efficient Solutions to Autonomous Mapping and Navigation Problems , 2009 .

[14]  Ronald Azuma,et al.  Orientation Tracking for Outdoor Augmented Reality Registration , 1999, IEEE Computer Graphics and Applications.

[15]  Sherali Zeadally,et al.  RFID Infrastructure Design: A Case Study of Two Australian RFID Projects , 2009, IEEE Internet Computing.

[16]  Kyuseo Han,et al.  Combination of RFID and Vision for Mobile Robot Localization , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[17]  Paul A. Beardsley,et al.  Sequential Updating of Projective and Affine Structure from Motion , 1997, International Journal of Computer Vision.

[18]  Leena Ukkonen,et al.  Passive UHF RFID in Paper Industry: Challenges, Benefits and the Application Environment , 2009, IEEE Transactions on Automation Science and Engineering.

[19]  Peter H. Veltink,et al.  Ambulatory Position and Orientation Tracking Fusing Magnetic and Inertial Sensing , 2007, IEEE Transactions on Biomedical Engineering.

[20]  Hiroyuki Morikawa,et al.  DOLPHIN: an autonomous indoor positioning system in ubiquitous computing environment , 2003, Proceedings IEEE Workshop on Software Technologies for Future Embedded Systems. WSTFES 2003.

[21]  Ronald Azuma,et al.  The Challenge of Making Augmented Reality Work Outdoors , 1999 .

[22]  J.R. Tuttle,et al.  Traditional and emerging technologies and applications in the radio frequency identification (RFID) industry , 1997, 1997 IEEE Radio Frequency Integrated Circuits (RFIC) Symposium. Digest of Technical Papers.

[23]  Henry H. Bi,et al.  RFID-Enabled Discovery of Supply Networks , 2009, IEEE Transactions on Engineering Management.

[24]  Toshifumi Tsukiyama,et al.  Global navigation system with RFID tags , 2002, SPIE Optics East.

[25]  Ivan E. Sutherland,et al.  A head-mounted three dimensional display , 1968, AFIPS Fall Joint Computing Conference.

[26]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[27]  K.R. Foster,et al.  RFID Inside , 2007, IEEE Spectrum.

[28]  Roy Want,et al.  Bridging physical and virtual worlds with electronic tags , 1999, CHI '99.

[29]  Yanyong Zhang,et al.  RollCall : The Design For A Low-Cost And Power Efficient Active RFID Asset Tracking System , 2007, EUROCON 2007 - The International Conference on "Computer as a Tool".

[30]  Daniel W. Engels,et al.  The reader collision problem , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[31]  Woodrow Barfield,et al.  Fundamentals of Wearable Computers and Augumented Reality , 2000 .

[32]  David W. Murray,et al.  Simultaneous Localization and Map-Building Using Active Vision , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Allen R. Hanson,et al.  Robust methods for estimating pose and a sensitivity analysis , 1994 .

[34]  Luc Van Gool,et al.  Tracking based structure and motion recovery for augmented video productions , 2001, VRST '01.

[35]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Jerry Zeyu Gao,et al.  Understanding 2D-BarCode Technology and Applications in M-Commerce - Design and Implementation of A 2D Barcode Processing Solution , 2007, 31st Annual International Computer Software and Applications Conference (COMPSAC 2007).

[37]  Eyal de Lara,et al.  Accurate GSM Indoor Localization , 2005, UbiComp.

[38]  Xinhua Zhuang,et al.  Pose estimation from corresponding point data , 1989, IEEE Trans. Syst. Man Cybern..

[39]  Marie-Odile Berger,et al.  Pose Estimation for Planar Structures , 2002, IEEE Computer Graphics and Applications.

[40]  Hungsun Son,et al.  Optimization of Measuring Magnetic Fields for Position and Orientation Tracking , 2011, IEEE/ASME Transactions on Mechatronics.

[41]  Timo Hämäläinen,et al.  Experiments on local positioning with Bluetooth , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[42]  Michael Gervautz,et al.  CCD‐Camera Based Optical Beacon Tracking for Virtual and Augmented Reality , 1996, Comput. Graph. Forum.

[43]  Ramakant Nevatia,et al.  Automatic Integration of Facade Textures into 3D Building Models with a Projective Geometry Based Line Clustering , 2002, Comput. Graph. Forum.

[44]  Jeong Geun Kim,et al.  A capture-aware access control method for enhanced RFID anti-collision performance , 2009, IEEE Communications Letters.

[45]  Per K. Enge,et al.  Global positioning system: signals, measurements, and performance [Book Review] , 2002, IEEE Aerospace and Electronic Systems Magazine.

[46]  Minos N. Garofalakis,et al.  Adaptive cleaning for RFID data streams , 2006, VLDB.

[47]  Daphne Koller,et al.  Using Learning for Approximation in Stochastic Processes , 1998, ICML.

[48]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[49]  Adrian David Cheok,et al.  Augmented Reality Camera Tracking with Homographies , 2002, IEEE Computer Graphics and Applications.

[50]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[51]  Jaideep Srivastava,et al.  Adaptive binary splitting for efficient RFID tag anti-collision , 2006, IEEE Communications Letters.

[52]  S. Yabukami,et al.  Wireless Magnetic Motion Capture System—Compensatory Tracking of Positional Error Caused by Mutual Inductance , 2007, IEEE Transactions on Magnetics.

[53]  Tadayoshi Kohno An Interview with RFID Security Expert Ari Juels , 2008, IEEE Pervasive Computing.

[54]  Sebastian Thrun,et al.  FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges , 2003, IJCAI 2003.

[55]  Samir Kouro,et al.  Unidimensional Modulation Technique for Cascaded Multilevel Converters , 2009, IEEE Transactions on Industrial Electronics.

[56]  Éric Marchand,et al.  Real-time markerless tracking for augmented reality: the virtual visual servoing framework , 2006, IEEE Transactions on Visualization and Computer Graphics.

[57]  Martin R. Gibbs,et al.  Mediating intimacy: designing technologies to support strong-tie relationships , 2005, CHI.

[58]  Jeff Kabachinski,et al.  An introduction to RFID. , 2005, Biomedical instrumentation & technology.

[59]  Toshifumi Tsukiyama,et al.  World map based on RFID tags for indoor mobile robots , 2005, SPIE Optics East.

[60]  JangMyung Lee,et al.  An Efficient Localization Algorithm for Mobile Robots based on RFID System , 2006, 2006 SICE-ICASE International Joint Conference.

[61]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[62]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[63]  Sudarshan S. Chawathe,et al.  Managing RFID Data , 2004, VLDB.

[64]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[65]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[66]  Tong Zhen,et al.  Notice of Violation of IEEE Publication PrinciplesA RFID Logistics Resource Management System for the Warehouses , 2009, 2009 International Conference on Environmental Science and Information Application Technology.

[67]  Seth J. Teller,et al.  The cricket compass for context-aware mobile applications , 2001, MobiCom '01.

[68]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[69]  Petar M. Djuric,et al.  Architectures for efficient implementation of particle filters , 2004 .

[70]  Ren Zhengang,et al.  Design of Electronic Toll Collection System in Expressway Based on RFID , 2009, 2009 International Conference on Environmental Science and Information Application Technology.

[71]  Jun Yang,et al.  Magnetic hand motion tracking system for human-machine interaction , 2010 .

[72]  Gaetano Borriello,et al.  SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength , 2000 .

[73]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[74]  Ronald Azuma,et al.  Hybrid inertial and vision tracking for augmented reality registration , 1999, Proceedings IEEE Virtual Reality (Cat. No. 99CB36316).

[75]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[76]  Jannick P. Rolland,et al.  A Survey of Tracking Technologies for Virtual Environments , 2001 .

[77]  Alessandro Pozzebon,et al.  An RFID Based System for the Underwater Tracking of Pebbles on Artificial Coarse Beaches , 2009, 2009 Third International Conference on Sensor Technologies and Applications.

[78]  Larry S. Davis,et al.  Model-based object pose in 25 lines of code , 1992, International Journal of Computer Vision.

[79]  Amnon Shashua,et al.  Threading Fundamental Matrices , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[80]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[81]  Axel Pinz,et al.  Building a hybrid tracking system: integration of optical and magnetic tracking , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[82]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[83]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.