RFID Tag-equipped Drone for Antenna Array-based Localization: A Step-by-Step MATLAB Simulation Tutorial

Arif Abdul Aziz; Muhammad Miftahul Amri

doi:10.56741/esl.v1i02.161

Authors

Arif Abdul Aziz
abdul.aziz@iistr.org
Independent Researcher, Indonesia
Muhammad Miftahul Amri Universitas Ahmad Dahlan, Indonesia https://orcid.org/0000-0001-5501-8484

Keywords:

radio frequency identifiers (RFID), drone, antenna array, localization, MATLAB

Abstract

In the last decades, Radio Frequency Identifiers (RFID) has gained massive popularity.In the last decades, Radio Frequency Identifiers (RFID) have gained massive popularity. RFID technology allows data to be encoded in a small form (tag). The data contained inside the RFID tag can be captured wirelessly by the RFID reader through electromagnetic (EM) waves. On the other hand, localization techniques have achieved significant developments in the last decade. There is a wide range of localization tool variations, including satellite-based localization (i.e., GPSS, GLONASS), Bluetooth Low Energy (BLE), IMU, camera, infrared sensor, and even RF-based localization. In this work, we present an accurate localization technique via battery-less passive RFID tag-UAV integration. We have employed two low-complexity methods: generalized cross-correlation with phase transformation (GCC-PHAT) algorithm and triangulation technique. By simulation, we have validated that an accurate antenna array-based dynamic localization can be realized. For the sake of simplicity, we only demonstrate two-dimensional movement. However, the identical method can also be used in the three-dimensional movement. While this paper considers only two antenna arrays and one RFID tag, the proposed concept can be expanded to a more extensive system with a larger number of antenna arrays and RFID tags.

Downloads

Download data is not yet available.

References

R. Bhattacharyya and P. Nikitin, ”Guest Editorial: Special Issue on IEEE RFID 2019 Conference,” in IEEE Journal of Radio Frequency Identification, vol. 4, no. 1, pp. 1-2, March 2020, doi: 10.1109/JRFID.2020.2973562.

A. Kayad, et al., ”Assessing topsoil movement in rotary harrowing process by RFID (Radio-Frequency Identification) technique,” in Agriculture, vol. 9, no. 8, August 2019, doi: 10.3390/agriculture9080184.

N. Ibragimov and A. Amangeldiyeva, ”Perspectives of Integration QR Codes and RFID readers in large-scale events controlled by HRM systems,” 2019 15th International Conference on Electronics, Computer and Computation (ICECCO), 2019, pp. 1-6, doi: 10.1109/ICECCO48375.2019.9043285.

P. Parthiban, B. -C. Seet and X. J. Li, ”Low-cost low-profile UHF RFID reader antenna with reconfigurable beams and polarizations,” 2017 IEEE International Conference on RFID (RFID), 2017, pp. 8187, doi: 10.1109/RFID.2017.7945591.

H. Yojima, Y. Tanaka, Y. Umeda, O. Takyu, M. Nakayama and K. Kodama, ”Analysis of read range for UHF passive RFID tags in close proximity with dynamic impedance measurement of tag ICs,” 2011 IEEE Radio and Wireless Symposium, 2011, pp. 110-113, doi: 10.1109/RWS.2011.5725440.

L. Ukkonen, L. Sydanheimo and M. Kivikoski, ”Read Range Perfor-¨ mance Comparison of Compact Reader Antennas for a Handheld UHF RFID Reader,” 2007 IEEE International Conference on RFID, 2007, pp. 63-70, doi: 10.1109/RFID.2007.346151.

C. H. Loo, et al., ”Experimental and Simulation Investigation of RFID Blind Spots,” in Journal of Electromagnetic Waves and Applications, vol. 23, no. 5-6, pp. 747-760, April 2012, doi:10.1163/156939309788019750.

G. Casati et al., ”The Interrogation Footprint of RFID-UAV: Electromagnetic Modeling and Experimentations,” in IEEE Journal of Radio Frequency Identification, vol. 1, no. 2, pp. 155-162, June 2017, doi: 10.1109/JRFID.2017.2765619.

S. M. Bae, K. H. Han, C. N. Cha and H. Y. Lee, ”Development of Inventory Checking System Based on UAV and RFID in Open Storage Yard,” 2016 International Conference on Information Science and Security (ICISS), 2016, pp. 1-2, doi: 10.1109/ICISSEC.2016.7885849.

D. S. Jasrotia and M. J. Nene, ”Localisation using UAV in RFID and Sensor Network Environment:Needs and Challenges,” 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2019, pp. 274-279, doi: 10.1109/ICCCIS48478.2019.8974536.

G. R. Gopal, B. D. Rao and G. P. Villardi, ”Access Point Placement for Hybrid UAV-Terrestrial Small-Cell Networks,” in IEEE Open Journal of the Communications Society, vol. 2, pp. 1826-1841, 2021, doi: 10.1109/OJCOMS.2021.3100060.

N. Proshkin, E. Basan and M. Lapina, ”Radio Frequency Method for Emulating Multiple UAVs,” 2021 17th International Conference on Intelligent Environments (IE), 2021, pp. 1-4, doi: 10.1109/IE51775.2021.9486599.

J. Lyu, Y. Zeng, R. Zhang and T. J. Lim, ”Placement Optimization of UAV-Mounted Mobile Base Stations,” in IEEE Communications Letters, vol. 21, no. 3, pp. 604-607, March 2017, doi: 10.1109/LCOMM.2016.2633248.

N. M. Drawil, H. M. Amar and O. A. Basir, ”GPS Localization Accuracy Classification: A Context-Based Approach,” in IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 262-273, March 2013, doi: 10.1109/TITS.2012.2213815.

A. Yassin et al., ”Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications,” in IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1327-1346, Secondquarter 2017, doi: 10.1109/COMST.2016.2632427.

H. Ma, M. Antoniou, A. G. Stove, J. Winkel and M. Cherniakov, ”Maritime Moving Target Localization Using Passive GNSS-Based Multistatic Radar,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4808-4819, Aug. 2018, doi: 10.1109/TGRS.2018.2838682.

B. Wang, Y. Wang, X. Qiu and Y. Shen, ”BLE Localization With Polarization Sensitive Array,” in IEEE Wireless Communications Letters, vol. 10, no. 5, pp. 1014-1017, May 2021, doi: 10.1109/LWC.2021.3055558.

A. Poulose, O. S. Eyobu and D. S. Han, ”An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data,” in IEEE Access, vol. 7, pp. 11165-11177, 2019, doi: 10.1109/ACCESS.2019.2891942.

M. Tian, Q. Nie and H. Shen, ”3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning,” 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 4211-4217, doi: 10.1109/ICRA40945.2020.9196940.

D. Yang, W. Sheng and R. Zeng, ”Indoor human localization using PIR sensors and accessibility map,” 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015, pp. 577-581, doi: 10.1109/CYBER.2015.7288004.

R. Elbakly and M. Youssef, ”A Robust Zero-Calibration RF-Based Localization System for Realistic Environments,” 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2016, pp. 1-9, doi: 10.1109/SAHCN.2016.7732967.