An RFID Indoor Positioning Algorithm Based on Support Vector Regression

Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

[1]  JangMyung Lee,et al.  An Efficient Localization Scheme for a Differential-Driving Mobile Robot Based on RFID System , 2007, IEEE Transactions on Industrial Electronics.

[2]  Ivan Marsic,et al.  Deep Learning for RFID-Based Activity Recognition , 2016, SenSys.

[3]  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).

[4]  Hee Yong Youn,et al.  Intelligent RFID tag detection using support vector machine , 2009, IEEE Transactions on Wireless Communications.

[5]  Peng Li,et al.  An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor , 2017, Sensors.

[6]  Yong Jun Zhang,et al.  Indoor Location Based on an LANDMARC Improved Algorithm , 2014, CIT 2014.

[7]  Alice Buffi,et al.  A Phase-Based Technique for Localization of UHF-RFID Tags Moving on a Conveyor Belt: Performance Analysis and Test-Case Measurements , 2015, IEEE Sensors Journal.

[8]  Stefano Chessa,et al.  Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems , 2013, ISPRS Int. J. Geo Inf..

[9]  Honggui Li,et al.  Low-Cost 3D Bluetooth Indoor Positioning with Least Square , 2014, Wirel. Pers. Commun..

[10]  Lingfei Mo,et al.  Two-dimension localization of passive RFID tags using AOA estimation , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.

[11]  Gang Li,et al.  Bandwidth dependence of CW ranging to UHF RFID tags in severe multipath environments , 2011, 2011 IEEE International Conference on RFID.

[12]  Ulrich Muehlmann,et al.  UWB ranging in passive UHF RFID: proof of concept , 2010 .

[13]  Yuriy S. Shmaliy,et al.  UWB-Based Indoor Human Localization With Time-Delayed Data Using EFIR Filtering , 2017, IEEE Access.

[14]  Jianqiang Wang,et al.  RFID-Based Vehicle Positioning and Its Applications in Connected Vehicles , 2014, Sensors.

[15]  Chang-Soo Park,et al.  TDOA-based optical wireless indoor localization using LED ceiling lamps , 2011, IEEE Transactions on Consumer Electronics.

[16]  Changzhi Wang,et al.  Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine , 2017, Symmetry.

[17]  Junghwan Kim,et al.  Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm , 2009, Sensors.

[18]  Samer S. Saab,et al.  A Standalone RFID Indoor Positioning System Using Passive Tags , 2011, IEEE Transactions on Industrial Electronics.

[19]  Davide Dardari,et al.  Analysis of UWB Tag Backscattering and Its Impact on the Detection Coverage , 2014, IEEE Transactions on Antennas and Propagation.

[20]  Siyuan Liu,et al.  COCKTAIL: An RF-Based Hybrid Approach for Indoor Localization , 2010, 2010 IEEE International Conference on Communications.

[21]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[22]  Chong Shen,et al.  A combined GPS UWB and MARG locationing algorithm for indoor and outdoor mixed scenario , 2019, Cluster Computing.

[23]  Davide Dardari,et al.  Millimeter-Wave Beamsteering for Passive RFID Tag Localization , 2018, IEEE Journal of Radio Frequency Identification.

[24]  Kubo.  The Research of IoT Based on RFID Technology , 2014, 2014 7th International Conference on Intelligent Computation Technology and Automation.

[25]  S. P. Raja,et al.  Internet of Things: Challenges, Issues and Applications , 2018, J. Circuits Syst. Comput..

[26]  Davide Dardari,et al.  A Low Complexity Scheme for Passive UWB-RFID: Proof of Concept , 2016, IEEE Communications Letters.

[27]  Yi Liu,et al.  Indoor Fingerprint Positioning Based on Wi-Fi: An Overview , 2017, ISPRS Int. J. Geo Inf..

[28]  José Neuman de Souza,et al.  Mathematical model for a collaborative indoor position system (IPS) and movement detection of devices within IoT environment , 2017, SAC.

[29]  D. A. Savochkin,et al.  RFID indoor positioning system based on read rate measurement information , 2013, 2013 IX Internatioal Conference on Antenna Theory and Techniques.