978-1-4799-0902-5/13/$31.00 ©2013 IEEE 505

Artificial Neural Networks for Ranging of Passive UHF RFID Tags

Marija Agatonovic1, Emidio Di Giampaolo2, Piero Tognolatti2, Bratislav Milovanovic1

Abstract – Ranging of passive Ultra High Frequency (UHF)

Radio Frequency Identification (RFID) tags in indoor environments is a topical issue nowadays. Due to complexity of such an environment, there is no effective solution to this problem. In this paper we investigate application of Artificial Neural Networks (ANNs) in indoor localization of passive UHF RFID tags. Namely, we estimate distance between a reader antenna and a couple of tags attached to an item, using nonlinear mapping that ANNs perform between measured values of the Received Signal Strength Indicator (RSSI), turn on power and phase on the one hand, and the distance on the other. The proposed ANN model calculates distance with an average error of 7.31 cm.

Keywords – ANNs, RFID, passive UHF RFID tags.

I. INTRODUCTION

Widely used in logistics, passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) systems have also found application in areas where context-aware information is desirable (e.g. localization and navigation of persons and autonomous vehicles) [1], [2]. The passiveness of these tags makes them cheap, almost maintenance-free and with practically no lifetime limit. The information stored in the memory of a tag is sent wirelessly to an interrogator device (i.e. a reader antenna) by means of a modulation of the radio-wave that, radiated by the reader, is backscattered by the tag (Fig. 1). The power needed to weak-up the tag and activate the modulation is supplied by the electromagnetic wave arriving from the reader. Since the power level radiated by the reader is limited to small values (because of the specific safety rules), readable distance of the tag is limited to few meters. This is not a limitation for localization and navigation purposes because in these applications the tag has to be readable only in the proximity of the point where the moving and operating tasks of an individual or an autonomous vehicle have to be done.

The main limit of the UHF RFID systems is estimation of distance between a tag and a reader. Because of the narrow bandwidth of RFID signals and the short distance involved, accurate ranging techniques like time of flight [3] and frequency modulated continuous wave radar [4] are not suitable.

1Marija Agatonovic and Bratislav Milovanovic are with the Faculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia (e-mail: marija.agatonovic@elfak.ni.ac.rs, bata@elfak.ni.ac.rs).

2Emidio Di Giampaolo and Piero Tognolatti are with the Department of Electrical Engineering, University of L’Aquila, Via G. Gronchi, 18 - Nucleo Industriale di Pile, 67100 L'Aquila (e-mail: emidio.digiampaolo@univaq.it, piero.tognolatti@univaq.it).

Also, dual-frequency [5] and multi-frequency [6], [7] continuous wave (CW) radar, which exploit the phase difference technique, cannot be used for this application as they require a large frequency band not available in the allowed RFID frequency bands (e.g. the EU band is 2.5 MHz wide while the band allowed in US is 21 MHz). Ranging techniques based on the Received Signal Strength Indicator (RSSI) have been frequently proposed, but they suffer from poor accuracy. On the contrary, phase-based ranging techniques [8] provide highly accurate results, but suffer from cycle ambiguity which makes them inappropriate to measure distances longer than a wavelength.

Since a RFID system, based on off-the-shelf technology, retrieves only the RSSI and the phase of the backscattered signal, accurate estimation of distance is not straightforward. To overcome this problem we have expanded the measurement data set by recording the turn-on power (i.e. the minimum power at the output port of the reader necessary to weak-up the tag) and have developed a method that exploits RSSI, turn-on power and phase for accurate estimation of the reader-to-tag distance. The method is based on a couple of cascade-connected Artificial Neural Networks (ANNs). The first network recovers the cycle ambiguity using RSSI and turn-on power measured data while the second network refines the estimated distance exploiting phase measurements and the output of the first network.

In this way, ANNs perform non-linear mapping between measured values (turn-on power, RSSI, phase) and the distance. Unlike most other localization techniques based on fixed mathematical calculations [3]-[8], ANNs are able to account for real environmental conditions such as multipath propagation effects, humidity, presence of other electronic devices, etc [9]-[10]. The prediction capability of the ANN RFID localization system allows it to accurately estimate the location of RFID tags in real scenarios. Another advantage of ANNs is their ability to give the distance estimation in real-time what is particularly suitable when the vehicle is moving.

Fig. 1. Passive UHF RFID system

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The paper is organized as follows. Section II describes the features of the proposed RFID system with an insight into the electromagnetic behaviour of passive UHF RFID tags in real environment. In Section III, the ANN model for distance estimation is proposed. Section IV describes the laboratory experiments. Modelling results are presented and discussed in Section V. Finally, Section VI concludes the paper.

II. INSIGHT INTO THE ELECTROMAGNETIC

BEHAVIOUR OF PASSIVE UHF RFID TAGS

Let us suppose that a couple of passive UHF RFID tags are attached to an item whose distance from the reader has to be estimated. The reader interrogates tags and gathers (besides the unique ID code of each tag) physical quantities of the received signals strictly related to the distance d between the reader and the tags. After handling the measured quantities by means of the proposed method, the reader system provides the distance. For an off-the-shelf system there are three possible measurable quantities: the turn-on power, the RSSI and the phase of the signals received from the tags. Assuming ideal conditions such as free-space environment, there are simple functional relationships between d and the measurable quantities, so that d can be easily retrieved using inversion of the equations. In real environment, multipath effects complicate the equations and ranging is not easily performed unless special techniques like ANNs are used.

Exploiting polarization diversity of the RFID signals we increase the available information gathered by the reader with a consequently improvement of the ability of ANN to estimate the distance. For this reason, any tagged object is equipped with a couple of orthogonally positioned and linearly polarized tags. The reader makes use of a circular polarized antenna to allow the interrogation of both the orthogonal tags. The backscattered signal of a tag distinguishes from that of the orthogonal one because it suffers from a different interaction with the environment. In practice, the signals distinguish for polarization being subject to different multipath effects, therefore the measurable quantities of the two orthogonal tags bring different information allowing the ANN to be more effective.

Since neural networks require a stage of training based on a set of measured data that depend on the environment (because of multipath), a constraint concerning the kind of environment where the proposed method is applicable has to be given. In fact, if the environment is time-varying then multipath effects change over time and the training of the network has to be repeated in order to track changes. Clearly this is not possible unless a re-training of the network is used. For this reason, we consider only time-invariant environments with deterministic multipath so that the training of the network remains effective for a long time. This is not a limitation of the effectiveness of the method because a number of environments share the time-invariant property. This is the case of all automated environments like mining tunnels, industrial plants, power stations, corridors of some special buildings where an autonomous vehicle passes through without moving any object or modifying the environment.

III. ANN MODEL

As mentioned in Section I, distance estimation of passive RFID tags in indoor environment suffers from noise and multipath propagation effects. To improve the accuracy of ranging of RFID tags, here we propose an ANN model.

The model is composed of two MLP neural networks trained to model dependence of the distance on the measured parameters such as turn-on power, RSSI and phase of the horizontally and vertically positioned RFID tag (Fig. 2). According to this, the first network (MLP1) is supposed to estimate raw distance from the turn-on power and RSSI while the second network (MLP2) refines this result using phase information for the horizontally and vertically positioned RFID tag.

Fig. 2. Proposed ANN model

The data used to train the ANN model are measured values obtained from experiments for certain number of distances between the tags and the reader antenna.

IV. EXPERIMENTAL SETUP

A couple of passive UHF RFID tags (LAB-ID UH331) [11] were attached to a large cardboard box (Fig. 3). These were orthogonally positioned dipole-like tags supporting linear polarization of electromagnetic waves. On the opposite side, the reader (M6e ThingMagic) was equipped with a circularly polarized antenna mounted on a wooden frame.

Fig. 3. Scheme of the proposed ranging method

Horizontal tag

Vertical tag

Reader antenna

Reader

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The tags and the reader antenna were set at the height of 1 m above the floor while the whole system was placed in a laboratory room with nearby many metallic scattering objects. All measurements were done for reader-to-tag distance between 1 m and 2.5 m in steps of 5 cm, at UHF frequency 868 MHz. For all positions of the reader antenna (or equivalently of cardboard box), turn-on power, RSSI and phase of each tag were recorded.

Fig. 4. Measured TURN ON power for horizontally and vertically

positioned tag

Fig. 5. Measured RSSI for horizontally and vertically positioned tag

Fig. 6. Measured phase (- horizontally positioned tag, -- vertically

positioned tag)

For each position, the reader interrogated the tags 30 times and the mean of the measured quantities was used for distance estimation. Measured values of turn-on power, RSSI and phase, are shown in Fig. 4, Fig. 5 and Fig. 6, respectively. It can be observed that multipath propagation phenomena in the laboratory environment strongly affect measurement results making the distance estimation nontrivial. In fact, the turn-on power and the RSSI, illustrated in Fig. 4 and Fig. 5, respectively, are far from being a smooth function of the distance while the phase (Fig. 6), besides the cycle ambiguity, has even lost the periodic behavior because of multipath.

V. ANN RESULTS

Development of the ANN-based RFID system is divided into two phases. The first phase includes training of the MLP1 network to provide raw distance of RFID tags. In the next step, information about phase is used to retrieve more accurate distance. The MLP2 network practically acts as a correction network by which the RFID system tries to provide the most accurate result possible.

Measured results obtained in the experiments (Section IV) are divided into two sets, the training and the test set. Among the collected results, 66.67% were used for the training of the ANN model while 33.33% were intended for testing. The training algorithm for both networks is the Levenberg - Marquardt (LM). Since the number of hidden neurons for both MLP neural networks cannot be a priori known, it is usually determined during the ANN training process. Namely, ANNs with different number of hidden neurons are trained (weights and biases are calculated) and compared, and according to the test statistics, the best networks are chosen. After the completed training process, the chosen ANNs can be used for fast ranging of the RFID tags. Distance can be estimated accurately for any input value belonging to the same range as the inputs used in the training process.

For the particular problem, the best MLP1 network contains 12 neurons in both hidden layers. The network is tested using the data from the test set and scattering diagram is shown in Fig. 7. The correlation coefficient of this network is 0.96, and average error in estimating distance is 8.77 cm (Table 1). The second MLP network (MLP2) in the model is smaller, composed of 2 hidden layers and 10 neurons in each of them. One can see in Fig. 8, that MLP2 network provides more accurate results than MLP1. The correlation coefficient of this network is 0.98, the average estimation error is reduced to 7.31 cm while the maximum error is improved for 11.64 cm (Table 1). According to these results, it can be concluded that phase of the backscattered RF signal can significantly contribute to improving accuracy of ranging of the RFID tags (it lowers the maximum estimation error). Also, it can be observed that the proposed ANN model successfully accounts for the environmental conditions as well as the behavior of the tags attached to an item made of a specific material (paper, in the particular case). To the best of our knowledge, no other technique can do more accurate ranging of UHF RFID tags using a commercial system.

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Fig. 7. Scattering diagram of the MLP1 neural network

Fig. 8. Scattering diagram of the MLP2 neural network

TABLE I

PERFORMANCE COMPARISON OF MLP NEURAL NETWORKS IN THE

CASCADE-CONNECTED ANN MODEL

Neural network

Worst Case Error (cm)

Average Case Error (cm)

MLP 1 26.33 8.77 MLP 2 11.64 7.31

VI. CONCLUSION

In this paper we have demonstrated that distance between a reader antenna and an item equipped with a couple of commercially available UHF RFID tags can be successfully estimated using an artificial neural network model. Measurements were performed in the laboratory environment with strong noise and multipath propagation effects present. Due to its nonlinearity and ability to embed the knowledge of the environment during the training process, the neural model is very efficient in estimating the distance. This investigation provides promising results for future research activities in this

field (especially for time-variant environment) as the ANN model can be re-trained with inputs from the changed environment.

ACKNOWLEDGMENT

The authors thank LAB-ID for providing tags used in the experiments.

REFERENCES

[1] T. Sanpechuda, L. Kovavisaruch, "A review of RFID localization: Applications and techniques," Proceedings of the 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2008), vol.2, May 2008, pp.769-772.

[2] E. Di Giampaolo and F. Martinelli, "Mobile robot localization using the phase of passive UHF-RFID signals," Industrial Electronics, IEEE Transactions on, vol. PP, no. 99, pp.1,1, 0 doi: 10.1109/TIE.2013.2248333.

[3] T. Xiong, J. Liu, Y. Yang, X. Tan, and H. Min, “Design and implementation of a passive UHF RFID-based Real Time Location System,” Proceedings of the 2010 IEEE Int. Symp. on VLSI Design Automation and Test (VLSI-DAT), April 2010, pp. 95–98.

[4] J. Heidrich, D. Brenk, J. Essel, G. Fischer, R. Weigel, and S. Schwarzer, “Local positioning with passive UHF RFID transponders,” Proceedings of the IEEE MTT-S Int. Microwave Workshop on Wireless Sensing, Local Positioning, and RFID, (IMWS 09), 2009, pp. 1–4.

[5] C. Zhou and J. D. Griffin, “Accurate Phase - Based Ranging Measurements for Backscatter RFID Tags,” IEEE Antennas and Wireless Propagation Letters, vol.11, pp.152-155, 2012.

[6] X. Li, Y. Zhang, and M. Amin, “Multifrequency-based range estimation of rfid tags,” Proceedings of the 2009 IEEE Internation Conference on RFID, 2009, pp. 147–154.

[7] D. Arnitz, K. Witrisal, and U. Muehlmann, “Multifrequency continuous wave radar approach to ranging in passive uhf rfid,” IEEE Transactions on Microwave Theory and Technique, vol. 57, no. 5, pp. 1398–1405, 2009.

[8] A. Ledeczi, P. Volgyesi, J. Sallai, B. Kusy, X. Koutsoukos, and M. Maroti, “Towards precise indoor RF localization,” Proceedings of the 5th Workshop on Embedded Networked Sensors, 2008.

[9] W.W.Y. Ng, Q. Yi-Song, L. Li ; D. Hai-Lan, P.P.K. Chan, D.S. Yeung, “Intelligent book positioning for library using rfid and book spine matching,” Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, Guilin, 10-13 July, 2011, pp. 465-470.

[10] A. Santos Martínez Sala, R. Guzman Quirós, E. Egea López, “Using neural networks and Active RFID for indoor location services,” Smart Objects: Systems, Technologies and Applications (RFID Sys Tech), 2010 European Workshop on, Ciudad, Spain, 15-16 June, pp. 1-9, 2010.

[11] http://www.lab-id.com