Open Access Open Access  Restricted Access Subscription Access
Arrangement of the antennas, RFID tags, antenna interrogation zones, and classification zones in the localization field

Fusing different measurements and algorithms to improve RFID localization accuracy

Dmytro A. Savochkin, Yu. B. Gimpilevich


This paper considers the topic of two-dimensional object localization using radio frequency identification (RFID) technology. One of the important problems that arise during the development of RFID positioning systems is choosing a localization algorithm and a type of measurement data. Usually developers try to find such a combination of an algorithm and measurement type that allows to achieve maximal localization accuracy for a particular scenario. However, in some situations there can be several combinations of algorithms and measurements with equally high accuracy. In order to simplify the choosing problem and to additionally increase accuracy it is developed a combinational localization method. The method is based on averaging position estimates formed by several point-based and zone-based algorithms that process different measurements. In our work there are used three point-based and three zone-based algorithms: a k nearest neighbors algorithm, trilateration, intersectional algorithm, the methods of support vector machine, artificial neural networks, and a naive Bayes classifier. As an input for the algorithms we utilized received signal strength, read rate, and proximity measurements. During the experiments we found that our method decreases the mean error by 15% and the maximum error by 14% compared to the best single algorithm.


radiofrequency identification; RFID; localization; positioning; measurement data; combinational method; received signal strength

Full Text:



ZHOU, J.; SHI, J. RFID localization algorithms and applications—a review. J. Intell. Manuf., v.20, n.6, p.695-707, 2009. DOI:

PRIYANTHA, N.B.; CHAKRABORTY, A.; BALAKRISHNAN, H. The Cricket location-support system. Proc. of MobiCom, Boston, MA, USA. 2000, p.32-43, DOI:

VEGNI, A.M.; BIAGI, M. An indoor localization algorithm in a small-cell LED-based lighting system. Proc. of IPIN, 13–15 Nov. 2012, Sydney, Australia. IEEE, 2012, p.1-7, DOI:

SUO, H.; WAN, J.; HUANG, L.; ZOU, C. Issues and challenges of wireless sensor networks localization in emerging applications. Proc. ICCSEE, 23–25 Mar. 2012, Hangzhou, China. IEEE, 2012, v.3, p.447-451, DOI:

YOUSSEF, M.; AGRAWALA, A. The Horus WLAN location determination system. Proc. MobiSys, Seattle, WA, USA. 2005, p.205-218. DOI:

BANKS, J.; PACHANO, M.; THOMPSON, L.; HANNY, D. The stage is set. In RFID Applied. Hoboken, NJ: Wiley, 2007, p.3-23, ISBN: 978-0-471-79365-6.

ZHANG, D.; XIA, F.; YANG, Z.; YAO, L.; ZHAO, W. Localization technologies for indoor human tracking. Proc. FutureTech, 21–23 May 2010, Busan, Korea. IEEE, 2010, p.1-6. DOI:

HUANG, Y.; BRENNAN, P.V.; SEEDS, A. Active RFID location system based on time-difference measurement using a linear FM chirp tag signal. Proc. PIMRC, 15–18 Sept. 2008, Cannes, France. IEEE, 2008, p.1-5, DOI:

NI, L.M.; LIU, Y.; LAU, Y.C.; PATIL, A.P. LANDMARC: indoor location sensing using active RFID. Wireless Networks, v.10, n.6, p.701-710, 2004. DOI:

CHENG, S.H. An indoor positioning system based on active RFID in conjunction with Bayesian network. Proc. ICMLC, 10–13 Jul. 2011, Guilin, China. IEEE, 2011, p.386-390. DOI:

ZHEN, Z.N.; JIA, Q.-S.; SONG, C.; GUAN, X. An indoor localization algorithm for lighting control using RFID. Proc. Energy 2030, 17–18 Nov. 2008, Atlanta, GA, USA. IEEE, 2008, p.1-6. DOI:

SAHA, S.; CHAUDHURI, K.; SANGHI, D.; BHAGWAT, P. Location determination of a mobile device using IEEE 802.11b access point signals. Proc. WCNC, 16–20 Mar. 2003, New Orleans, LA, USA. IEEE, 2003, v.3, p.1987-1992. DOI:

KOTSIANTIS, S.B. Supervised machine learning: a review of classification techniques. Proc. of Conf. on Emerging Artificial Intelligence Applications in Computer Engineering, v.160, Amsterdam, Netherlands. Amsterdam: IOS Press, 2007, p.3-24,

BRCHAN, J.L.; ZHAO, L.; WU, J.; WILLIAMS, R.E.; PÉREZ, L.C. A real-time RFID localization experiment using propagation models. Proc. IEEE RFID, 3–5 Apr. 2012, Orlando, FL, USA. IEEE, 2012, p.141-148, DOI:

SHIREHJINI, A.A.N.; YASSINE, A.; SHIRMOHAMMADI, S. An RFID-based position and orientation measurement system for mobile objects in intelligent environments. IEEE Trans. Instrum., Meas., v.61, n.6, p.1664-1675, 2012. DOI:

NI, L.M.; ZHANG, D.; SOURYAL, M.R. RFID-based localization and tracking technologies. IEEE Wireless Commun., v.18, n.2, p.45-51, 2011. DOI:

SUBRAMANIAN, S.P.; SOMMER, J.; SCHMITT, S.; ROSENSTIEL, W. RIL—reliable RFID based indoor localization for pedestrians. Proc. SoftCOM, 25–27 Sept. 2008, Split, Croatia. IEEE, 2008, p.218-222. DOI:

LAARAIEDH, M.; YU, L.; AVRILLON, S.; UGUEN, B. Comparison of hybrid localization schemes using RSSI, TOA, and TDOA. Proc. European Wireless, 27–29 Apr. 2011, Vienna, Austria. IEEE, 2011, p.1-5,

MACII, D.; COLOMBO, A.; PIVATO, P.; FONTANELLI, D. A data fusion technique for wireless ranging performance improvement. IEEE Trans. Instrum., Meas., v.62, n.1, p.27-37, 2013. DOI:

SAVOCHKIN, D.A. Combinational RFID-based localization using different algorithms and measurements. Proc. MIKON, 16–18 Jul. 2014, Gdansk, Poland. IEEE, 2014, p.563-566. DOI:

LIU, H.; DARABI, H.; BANERJEE, P.; LIU, J. Survey of wireless indoor positioning techniques and systems. IEEE T SYST MAN CY C, v.37, n.6, p.1067-1080, 2007. DOI:

GIMPILEVICH, Y.B.; SAVOCHKIN, D.A. RFID indoor positioning system based on read rate measurement information. Proc. ICATT, 16–20 Sept. 2013, Odessa, Ukraine. IEEE, 2013, p.546-548, DOI:

KHEDO, K.K.; SATHAN, D.; ELAHEEBOCUS, R.; SUBRAMANIAN, R.K.; RUGHOOPUTH, S.D. Overlapping zone partitioning localisation technique for RFID. Int. J. of UbiComp, v.1, n.2, p.20-32, 2010. DOI:

CACERES, M.; SOTTILE, F.; SPIRITO, M.A. WLAN-based real time vehicle locating system. Proc. VTC Spring, 26–29 Apr. 2009, Barcelona, Spain. IEEE, 2009, p.1-5, DOI:

MITCHELL, T.M. Naive Bayes classifier. In Machine Learning. McGraw-Hill SEM, 1997, p.177-180.

LIU, C.-L.; HAO, H.; SAKO, H. Confidence transformation for combining classifiers. Pattern Anal. Applic., v.7, n.1, p.2-17, 2004. DOI:

GIMPILEVICH, Y.B.; SAVOCHKIN, D.A. Simulation of measuring data obtained from RFID-tags in systems of spatial localization of objects. Radioelectron. Commun. Syst., vol. 59, no. 7, pp. 301–308, 2016. DOI:



  • There are currently no refbacks.

© Radioelectronics and Communications Systems, 2004–2017
When you copy an active link to the material is required
ISSN 1934-8061 (Online), ISSN 0735-2727 (Print)
tel./fax +38044 204-82-31, 204-90-41