novel bridge-loop reader for positioning with hf rfid under sparse tag grid

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 61, NO. 1, JANUARY 2014 555

Novel Bridge-Loop Reader for PositioningWith HF RFID Under Sparse Tag Grid

Mohd Yazed Ahmad and Ananda Sanagavarapu Mohan, Senior Member, IEEE

Abstract—We propose a novel method of positioning using ahigh-frequency radio-frequency identification (HF RFID) systemfor navigating autonomous vehicles when a sparse grid of floortags is employed. For this, we propose a novel triangular multi-loop-bridge reader antenna which generates an error signal in theform of bridge potential that is a function of the tags’ location.The proposed positioning algorithm combines the informationfrom the reader database with the error signals generated bythe bridge loop and couples them with any available data fromthe wheel encoders of an autonomously moving object to deduceits position and orientation. The accuracy and efficacy of theproposed algorithm are evaluated using both simulations andexperiments using an autonomous wheelchair. The results indicatethat the proposed method offers a significant improvement overexisting HF-RFID-based positioning methods for larger floor-tag-grid separation.

Index Terms—Autonomous wheelchair navigation, bridge po-tential (BP), high-frequency (HF) radio-frequency identification(RFID), localization, multiple-loop reader antenna.

I. INTRODUCTION

THE radio-frequency identification (RFID) technology of-fers simple, cost-effective, and reliable means for real-

time localization, positioning, and tracking of autonomousmoving vehicles such as wheelchairs, robots, etc. [1]–[5].

Traditional positioning methods use dead reckoning systemsin which the measurement data either from wheel encodersor inertial measurement units are utilized [7], [8]. However,the dead reckoning technique suffers from accumulated errorswhere estimation of position and orientation can deteriorateover the traveled distance. Limitations of dead reckoning sys-tems are typically compensated by using information fromadditional sensors such as cameras, ultrasonic sensors, lasers,etc. [9]–[12]. However, the deployment of sensors can beconstrained by special requirements such as line of sight, higherillumination levels, etc., which may be difficult to ensure in

Manuscript received May 28, 2012; revised September 6, 2012; acceptedJanuary 8, 2013. Date of publication March 7, 2013; date of current versionJuly 18, 2013. The work of M. Y. Ahmad was supported by the Ministry ofHigher Education Malaysia and by the University of Malaya.

M. Y. Ahmad is with the Center for Health Technologies, School ofElectrical, Mechanical and Mechatronic Systems, Faculty of Engineering andInformation Technology, University of Technology, Sydney, N.S.W. 2007,Australia, and also with the Department of Biomedical Engineering, Facultyof Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia (e-mail:[email protected]; [email protected]).

A. S. Mohan is with the Center for Health Technologies, School of Elec-trical, Mechanical and Mechatronic Systems, Faculty of Engineering andInformation Technology, University of Technology, Sydney, N.S.W. 2007,Australia (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIE.2013.2245617

all the user environments [13], [14]. In addition, the use ofadditional sensors could increase the complexity of positioningalgorithms.

RFID technology can be categorized by its operationalfrequency band. For positioning of a moving vehicle, high-frequency (HF) RFID with passive floor tags is commonlyemployed [1], [10], [15] as it can provide relatively accu-rate position estimation and can be reliably operated in mostenvironments. In addition, HF RFID uses near field whichis confined close to its reader antenna, thereby resulting inlower interference and lower radiated power. These desirablefeatures are particularly suited when operating in healthcarefacilities and other indoor scenarios [1], [17]. In view of theseadvantages, HF RFID has gone through many significant tech-nological improvements [18]–[20].

Typical HF-RFID-based positioning of a moving object uti-lizes floor tags with reader antenna usually fixed to the baseof the object. All the tag information is stored in the readerdatabase, and as soon as the reader antenna detects a tag thatfalls within the reader recognition area (RRA), its position isretrieved from the database. Unknown position of the objectis calculated using the retrieved coordinates of the detectedtag(s). Other approaches such as the use of multiple readers[21], additional sensors [10], and a host of other alternatives [1],[6], [15], [22], [23] have also been proposed. However, noneof these methods could completely remove the uncertaintiesin the RRA. Usually, a dense grid of floor tags with smallintertag separations is employed to reduce the uncertainty ofpositioning [15]. However, to obtain a cost-effective, flexible,and easily expandable floor-tag infrastructure, a sparse tag gridis a suitable option which requires fewer tags to be placedat important locations. However, when a sparse tag grid isemployed, efficient methods to overcome the uncertainties inthe RRA for improving positioning accuracy are necessary.

In this paper, we describe a new method to reduce uncertaintyof RRA when a sparse grid of floor tags is employed. We intro-duce the concept of bridge potential (BP) and propose a noveltriangular-loop-bridge (TLB) reader antenna using multiple tri-angular loops. The proposed TLB reader antenna can generatea bridge (error) signal as a function of the position of the floortags with respect to the boundaries of its RRA. Coupling thiserror signal with the available tag position information fromthe reader database as well as any available wheel encoderdata, an efficient but simple localization algorithm is proposedthat can estimate the position and orientation of a movingautonomous object. Our main contributions in this paper in-clude the following: 1) the introduction of bridge concept usingmultiple triangular loops to reduce reader uncertainty; 2) the

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556 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 61, NO. 1, JANUARY 2014

Fig. 1. Positioning using HF RFID: (a) using conventional loop reader an-tenna and (b) using the proposed TLB reader antenna.

novel position and orientation algorithms; and 3) improvementof overall positioning accuracy, particularly under sparser taggrid separation.

This paper is organized as follows. In Section II, thelimitations of the existing and commercially available HF-RFID-based positioning systems are highlighted. Section IIIintroduces the components of our proposed HF-RFID-reader-based positioning system and proposes the design of the novelTLB reader antenna. In Section IV, methods to manipulate theRRA of the proposed TLB antenna to improve positioning arediscussed. Section V details our proposed positioning algorithmincorporating the proposed TLB reader antenna. The experi-ments conducted on an autonomous wheelchair are describedin Section VI, which is followed by results and discussions inSection VII. Finally, Section VIII provides key conclusions ofthe work reported in this paper.

II. LIMITATIONS OF EXISTING METHODS

Typical HF-RFID-based positioning of a moving object uti-lizing floor tags is illustrated in Fig. 1(a). The object to belocalized typically is equipped with an HF RFID reader whoseantenna is placed at the base of the object. Passive tags areinstalled within the area to be localized, and their positionsare known. Whenever the reader recognizes the tag(s) locatedwithin its recognition area, the object position can be estimated.Typically, position is estimated either by averaging all thecoordinates of the detected tag(s) or averaging of the minimumand maximum coordinates of the detected tag(s) [10], [15]when employing RFID. Other methods such as nearest neigh-bor approximation, probabilistic methods, etc., have also beenproposed [22], [23]. The common limitation suffered by allthese techniques is that their localization uncertainty dependson the density of the tags and the size of RRA. In general, largerRRA allows lesser tag density, but the obtainable positioningaccuracy may have to be compromised.

To overcome some of these problems, various methods havebeen proposed in the literature. Han et al. [15] used a triangularpattern of tag arrangement and proposed orientation-estimationalgorithm based on motion continuity. Other methods to obtainoptimal recognition area for RFID-based system are also avail-able in the literature [23]. Park and Hashimoto [1] initially used

the tag’s coordinates with trigonometric functions to localizean autonomous mobile robot but later improved their systemby including the tag read time [6]. Methods of using additionalsensors have also been proposed, for example, Choi et al. [10]employed nine units of ultrasonic sensors installed at the frontside of a moving object to improve localization. For detectingtags, the recognition area of the reader antenna plays an impor-tant role. Attempts to improve the read range of HF RFID readerantenna have also been reported [24], [25]. However, methodsare required that can gainfully manipulate the recognition areaof the reader antenna to improve positioning and localization.Such methods, to the best of our knowledge, have not beenaddressed sufficiently in the open literature.

In this paper, we propose a localization method based on themanipulation of the recognition area of the HF RFID readerantenna. To this end, we propose a novel triangular multiple-loop reader antenna whose recognition area can be divided intomultiple smaller zones, with each zone corresponding to themagnetic field from a set of loops. Such multiple recognitionzones allow the system to have additional information to de-termine as to which of the zones (in turn, their correspondingloops) are closer to a detected tag(s), thereby helping to improvepositioning.

In general, multiple recognition zones can be achieved ei-ther by connecting multiple antennas to multiple readers or,alternatively, using switching techniques [26], [27]. The formerapproach requires more than one reader, which is usuallynot desirable, while the latter approach may degrade readperformance due to losses in switching. To overcome theseproblems, we have developed a novel bridge-loop reader an-tenna configuration [28]. In this paper, we propose to employmultiple triangularly shaped loops to be connected in a bridgeconfiguration so as to obtain error potential that corresponds tothe position of a detected tag.

Our proposed method requires only a single tag detectedper measurement. The knowledge of the error potential as afunction of the tag’s location within the reader’s recognitionarea helps to obtain larger intertag separation on the floor,thus leading to sparser floor grid. In addition, our proposedalgorithm utilizes both the readily available wheel encoderdata and the information of tags from the reader’s database tofurther improve the estimation of position and orientation of amoving object. Thus, continuous positioning can be achievedwith sparse floor-tag grid while ensuring accuracy.

III. PROPOSED HF-RFID-READER-BASED

POSITIONING SYSTEM

The proposed HF-RFID-reader-based positioning system isillustrated in Fig. 1(b). In this paper, we use a semiautonomouswheelchair to test our RFID-based positioning system. Themain components of the system are described as follows.

A. Proposed TLB Reader Antenna

The proposed TLB reader antenna consists of four triangu-larly shaped loops grouped into two sets so as to bifurcate theRRA by forming two subareas, as illustrated in Fig. 2. The

AHMAD AND MOHAN: NOVEL BRIDGE-LOOP READER FOR POSITIONING WITH HF RFID UNDER SPARSE TAG GRID 557

Fig. 2. Schematic diagram of the proposed triangular bridge-loop antenna andsystem interconnection.

loops are arranged in such a way that they represent the fourarm elements of a Wheatstone bridge (X1, X2, X3, and X4),as schematically shown in Fig. 2. The antenna is impedancematched to 50 Ω using passive components with serial–parallelconfiguration [24].

Usually, the reader antenna is fixed to the base of the objectto be localized so that it lies on a plane that is parallel to theplane of the floor on which tags are positioned. The tags aredetected by the reader whenever any tag lies within the RRAof its antenna. The RRA of the proposed TLB reader antennahas two zones, each one corresponding to the magnetic field ofeither combination of loops X1 and X4 or X2 and X3, as shownin Fig. 2. If a tag lies just below the reader antenna, it may fallwithin either Zone-1 or Zone-2, as shown in the same figure. Insuch a case, a change in impedance (ΔX) will be created thatwill cause a BP to be developed across the bridge terminals.

A prototype of the proposed TLB reader antenna is shownin Fig. 3(a). The prototype antenna has dimensions of Want =32 cm × Lant = 23 cm with a track width of 4.8 mm and a trackclearance of 1.2 mm. A metal plate whose size is comparableto that of the reader antenna is used as a shield to minimizeinterference. A gap of 3 cm between the shielding plate andthe antenna is maintained. The aforementioned dimensions arechosen so that the antenna fits onto the base of a moving object,which, in our case, is a semiautonomous wheelchair.

Measured and simulated results on the variation of theBP are shown in Fig. 3(b) when tags are located on thex−y plane at z = −6 cm and placed parallel to the plane ofthe reader antenna. Three series of bridge signals are recordedwhen tags are placed at different locations along the y-axis, asshown in the same figure. As can be seen in Fig. 3(b), a goodagreement is achieved between simulated and measured datafor various tag positions. Fig. 3(c) shows the overall changes ofthe bridge signal for different tag locations with respect to theRRA obtained only using simulations. The simulation resultsare obtained using a full-wave electromagnetic package FEKOthat is available commercially [29].

Fig. 3. (a) Prototype of the proposed TLB reader antenna. (b) Measured andFEKO-simulated data on the changes of the BP for different tag positions placedbelow the TLB antenna. (c) Overall BP.

B. Data Acquisition Unit

The data acquisition unit consists of a signal conditionerand an 8-bit microcontroller that are commercially available.The unit is used to acquire the bridge signal and feed it to the“Positioning Controller” (PC) unit, as illustrated in Fig. 2.

C. HF RFID Reader and Passive Tag

We employ a standard commercially available HF RFIDreader “TRF7960” and passive tags “RI-I03-114” from TexasInstruments. The reader allows recognition of various types oftag protocols, and it is equipped with anticollision algorithmsto allow detection of multiple tags.

D. PC

The PC represents a processing unit capable of receivingdata from RFID reader and data acquisition units. Our proposedpositioning algorithm (that will be explained later in Section V)is executed within this PC. The outputs of the PC unit are theposition and orientation of the object.


IV. MANIPULATION OF THE RRA

A. RRA

RRA is one of the key factors contributing to positioningaccuracy in HF-RFID-based system [1], [10], [23]. It is definedas an area on a tag plane over which the magnetic field from thereader antenna is sufficiently strong so as to interrogate nearbytag(s) located on the same plane.

Depending on the tag’s sensitivity and the distance “hant”between the plane of the reader antenna and the tag’s plane(floor in our case), the size of the RRA may vary. In certaincases involving sensitive tags, when the tag’s plane is closerto the antenna’s plane (i.e., hant < 3 cm), multiple RRA zonesmay be formed, typically beyond the projected antenna bound-ary onto the floor. In this paper, we assume that the plane of thereader antenna is always parallel to the tag’s plane (the floor inour case) so that the centers of the RRA and the antenna arealways aligned.

As far as the shape and location of the RRA are concerned,we approximate that, for a reasonable clearance between thefloor and reader antenna (i.e., 3 cm < Hant < 8 cm), the RRAis generally located on the floor parallel to and directly belowthe reader antenna and has a near rectangular shape closelyresembling the physical shape of the outer antenna boundary.

This approximation is reasonable since significant amount ofnear magnetic field produced by the HF RFID reader antennafalls within this area; therefore, most of the tags can be interro-gated successfully when present within this region.

The RRA of the reader antenna can be estimated by consid-ering the H-field over regions on the plane of the tags (whichis same as the floor in our case) that has the field strengthexceeding the minimum threshold required to interrogate thetags. The threshold value can vary for different tags, whichis available from the tag manufacturers. Here, we employtags made by Texas Instruments (RI-I03-114), which require aminimum magnetic field of 223 mA/m as per the data providedby the manufacturer.

The size and the shape of RRA would play an important rolein the proposed positioning algorithms. The RRA dimensions(W and L) for a given reader antenna are derived from theminimum magnetic field required to interrogate a particular tag,as explained earlier. In our proposed positioning system, thesepredetermined parameters are stored within the system memoryof the PC so that the positioning algorithm will automaticallychoose the appropriate RRA dimensions as per the type of thedetected tag.

B. RRA of TLB Versus Conventional Loop Reader Antenna

It would be useful to verify and compare the performance ofthe proposed TLB reader antenna with that of any conventionalloop antenna employed by commercially available HF RFIDreaders by examining the induced magnetic field “H” on thetag’s plane. FEKO [29] is used to accurately estimate theinduced H-fields for both antennas that have similar dimensions(Lant ×Want). An input power of 200 mW is used for bothantennas. The antennas are positioned on an x−y plane atz = 0 cm. The z-component of the H-field at a tag plane defined

Fig. 4. Comparison of H-fields of (a) TLB reader antenna and (b) conven-tional loop reader antenna. (c) Comparison of computed H-fields with measureddata for TLB reader antenna. (d) Computed H-fields of TLB reader antennawhen operated close to metallic structures.

by z = −6 cm is then computed. The H-field also represents theRRA of the antennas, as shown in Fig. 4(a) and (b). It can beseen that the RRA of the proposed TLB antenna is comparableto that of the chosen conventional reader antenna. It can also beseen from the figure that the shape of the RRA is approximatelyequal to the shape of the reader antenna in both cases, whichvalidates our assumption. The boundary on which the field hassufficient amplitude for detecting a tag is highlighted with darkline in each figure.

The computed H-fields of the proposed antenna obtainedusing FEKO are compared with experimental data measuredalong the x- and y-axes at the tag plane defined by z = −6 cm,


Fig. 5. Positioning error: (a) with a conventional reader antenna and (b) withthe proposed TLB reader antenna.

and the results are shown in Fig. 4(c). The close agreement be-tween simulation and the measured data also serves to validatethe accuracy of computational results. Encouraged by this, wecompute the H-fields radiated by the proposed antenna whenoperating close to metallic structure of both wheelchair andconcrete flooring, which is presented in Fig. 4(d). Note that,in Fig. 4(d), only the bottom part of the wheelchair is shown.These results highlight that the proposed TLB antenna canproduce the desired RRA and that, even when operating closerto metallic structures, the induced H-field is still of sufficientmagnitude to interrogate a chosen tag. More importantly, theseresults justify our initial assertion that the shape of the RRA iscomparable to the size of the reader antenna.

C. Positioning Error

Position estimations 1) using a commercially available con-ventional loop reader antenna and 2) using the proposed TLBreader antenna are shown schematically in Fig. 5(a) and (b),respectively. Note that the subscripts “C” and “β” represent theconventional and TLB antennas, respectively. We assume thatboth antennas have the same maximum uncertainty representedby a circle of radius “r.” We also assume that the floor tags aresparsely arranged on a grid so that, at any time instant, onlyone tag is detected by the reader. Let us define rβ as the radialdistance between Xβ and the detected tag position, as shown inFig. 5(b). The positioning errors can be represented by

eC = |X −XC | ≤ r (1)

eβ = |X −Xβ | ≤ r − rβ (2)

where X and X{C or β} are the actual and estimated positionsof the object, respectively.

For positioning with HF RFID that employs a conventionalloop reader antenna, the key parameter is the position of the de-tected tag with respect to its RRA. In this case, the positioningerror can vary from zero up to a maximum uncertainty equalto the radius “r” of the RRA. However, when the proposedTLB reader antenna is used with any existing HF RFID readers,additional information is available in the form of BP. The BPallows a correction to be applied to reduce the positioning errorso that it lies anywhere between zero and (r − rβ). Thus, the

use of the proposed TLB reader antenna reduces the positioninguncertainty. Methods to obtain rβ are presented in Section V.

D. Increasing the Tag Sparsity Without Reducing Accuracy

The floor tags are usually arranged in a dense rectangulargrid with close separation distance so that tags can be read atclosely spaced intervals by the reader. For a sparse grid of floortags, the tag separation has to be large, and hence, it must beensured that, at any instant, at least one tag is detected by thereader, so that the positioning accuracy is not degraded.

To achieve this, the RRA of the reader antenna can be madelarger by increasing the antenna’s physical size [24]. Withlarger RRA, tags can be positioned over larger distances, andhence, the floor-tag grid separation can be made equal to themaximum dimension “W ” of the reader antenna.

One of the limitations of HF RFID is the inability to detecttags placed very close (typically < 1 cm) to metallic objects.This can potentially cause the reader to miss the tags, whichthen affects the corresponding bridge signal measurement.One of the ways to minimize this is to install tags at somedistance away from obvious metallic objects in a given userenvironment. However, the proposed technique overcomes thisproblem by combining the object position information fromthe wheel encoders with the RFID tag readings. Our aim hereis to effectively utilize these two independent measurementsto compensate and overcome limitations of each other so thatpositioning accuracy is not degraded when employing sparsefloor-tag-grid infrastructure.

Sparse tag grid can be achieved by increasing the intertagseparation of the floor tags so that at least one tag is detected bythe reader while moving over short distances (1–2 m). To ensurethis, tags need to be placed along the expected travel path ofthe object under consideration. This can be useful in scenarioswhen the object under consideration moves as follows: either1) along a narrow pathway or indoor corridors or 2) along a pre-defined path (in factory floors or warehouses, etc.). In betweenany two consecutive tags, when no direct reading is made,the position can be estimated from the readily available wheelencoder data with a reference taken from previously estimatedposition of detected tags stored in the RFID database. The useof information from RFID database minimizes any unwantederrors associated with the wheel encoder data. Thus, the numberof tags to be deployed on the floor can be minimized, resultingin a simplified deployment without sacrificing the positioningaccuracy.

V. POSITIONING WITH THE PROPOSED

TLB READER ANTENNA

Our proposed positioning algorithm uses geometric approachto match boundaries of RRA with the time flags of key eventsthat occur during the detection of tags, as depicted in Fig. 6.When employing the proposed TLB antenna for sparse floor-tag-grid infrastructure, the positioning can be categorized intotwo operational modes, viz., Mode-1 and Mode-2.

This categorization takes into consideration the availableinformation of the currently detected tag at the PC. In Mode-1,position estimation is performed using only the BP plus the tag


Fig. 6. Key parameters for positioning with the moving TLB reader antenna.(a) TLB antenna and its RRA. (b) Illustration over the time period when the ithtag is detected.

Fig. 7. Flipped and scaled RRA, Path of the Object, and intersection points.

position information from the RFID database. Under Mode-2,all the information that is available under MODE-1 is included,and in addition, the information from object dynamics is alsoutilized for estimating the position. It is not difficult to guessthat the use of Mode-2 is always desirable as it can obtain im-proved accuracy of estimation. However, Mode-1 can be usefulfor a very basic system when no dynamic motion informationis available.

A. Key Parameters Involved in Positioning of a MovingReader When Using the TLB Antenna

We first define the key parameters used in the proposedalgorithms as follows. Figs. 6 and 7 illustrate the relationshipof these parameters with RRA as well as key time events whenan object that is carrying the reader moves over, for example,the “ith tag.”

The projected RRA on the floor (tag) plane is assumed to beparallel to the reader antenna, with the center point of the RRAaligned with the center of the antenna. We refer to this centerpoint as the position of the moving object.

The measurements are recorded continuously, and the dataare buffered into the memory of the PC so that all the requireddata are readily available as inputs to the proposed positioningalgorithm. The algorithm requires that these data be sampledat closer time intervals, depending on the speed of the object.The minimum time interval is determined by delays in sensormeasurement, typically being less than 3 ms.

All the measured data are correlated in time. Thus, a set ofdata from different measurements for a specific event can beacquired when the time flag of the event is known.

1) Time-Flag-of-Tagi (ta, tb, tc, td, and tf ): Each timeflag represents a key event when the reader antennamoves over a particular tag placed on the floor. Thisalso corresponds to the movement associated with theboundaries of the RRA of the reader antenna with re-spect to the tag and the resulting bridge signal variationassociated with the position of the tag with respect toRRA. The parameter ta is the time instant at which thecurrent tag gets detected (i.e., just after the RRA of thereader antenna moves over the tag), and tf is the timewhen the reader antenna moves off that tag. The tc andtd indicate the times that correspond to the transition ofthe tag between the boundaries of Zone-1 and Zone-2 ofthe moving RRA. The tb and te are the time flags thatoccur when the midpoints of the boundaries of Zone-1and Zone- 2 are reached, respectively. The relationshipof these time flags with the changes in the bridge signalis illustrated in Fig. 6(b). These time flags are obtainedusing both the information from the database of themoving RFID reader and the data on BP variation when aparticular tag, for example, the ith tag (tagi), is detected.

2) Trace-of-Tagi: It indicates the changes occurring in thelocation of the detected tag with respect to the trajectoryof the moving RRA within that time frame ta to tf . Itis important as it indicates the orientation of RRA withrespect to the position of the detected tagi.

3) Path of the Object (POi): It is a curve connecting aseries of points tracing the position of the object as itmoves during the time frame ta to tf with respect totagi. The POi is obtained from the information derivedfrom the wheel encoder within the specified time frame.It is related to the “Trace-of-Tagi” and useful for theestimation of the position of the object relative to thecoordinates of the detected “tagi.”

4) Points of intersection (P a, P c,d, and P f ): These are thepoints on the path of the object (POi) corresponding tothe time flags of tagi, as illustrated in Fig. 7. These pointsmust fall on the boundary of the RRA#, which is a scaledimage of the original RRA, as illustrated in Fig. 7.

5) TLB correction factors (rβ and θβ): These parametersare used to obtain the relative position of the object withrespect to the detected ith tag, as shown in Fig. 7. Therβ is the radial distance, as defined previously in (1).


The θβ is the angle between the radial line drawn in thedirection of rβ and the middle line along the XTLB-axiswhich crosses the center of the antenna, as illustrated inFigs. 6(a) and 7.

B. Mode-1 Position Estimation

Mode-1 is suitable for any basic system for which the wheelencoder data are not available. In such a case, the system usesonly the variations in the BP and the coordinates of the detectedtag available in the reader database to calculate the currentposition of the object. The BP will be continuously monitored,and whenever a moving reader detects a tag, the bridge signal atthat time instant is compared with a reference (see Fig. 6). Themagnitude and polarity of the BP signal help to decide whetherthe tag is located closer to Zone-1 or Zone-2 of the RRA.Once a zone is identified, its centroid is calculated to give theestimated position of the object. This simple approach leads toimprovement of the estimation of the position when operatingunder sparse tag grid as compared to some existing methods[15], [22]. When using this mode, the orientation of the objectcan be approximated following the method discussed in [1],which utilizes only the previous and current tag positions.

C. Mode-2 Position Estimation

In this paper, our focus is on Mode-2 to obtain higher accu-racy. In this mode, in addition to the information from RFIDmeasurements (recorded information from reader database andacquired BP from TLB antenna), the wheel encoder data arealso utilized to estimate the position.

In particular, the key aspect of Mode-2 is the determination ofthe TLB correction factors (rβ and θβ) so that the object’s rela-tive position with respect to a detected ith tag can be estimated.The true location of the object is obtained by adding the object’srelative position to the coordinates of the ith tag that is availableat the reader database. In the following, we describe the step-by-step process involved in the estimation of the position.

1) Extraction of key parameters from sensors: When a TLBreader antenna moves over a tag, variations in its BP allowthe system to recognize different time flags, viz., (ta, tb,tc, td, and tf ). Using ta and tf time flags as markers,the system obtains the path of the object (POi) from theencoder data.

Similarly, referring to Fig. 7, the intersection points(Pa, Pc,d, and Pf ) can be obtained from the encoder dataat times ta, (tc + td)/2, and tf . These intersection pointslead to the formation of straight lines lac, ldf , laf , as canbe seen from the same figure.

The POi that is obtained from the encoder data caneither be a straight line or a curve, depending on how theobject that carries the reader moves. However, for the sakeof generality, we consider a curvilinear path. It must bepointed that any accumulated drift or offset in the encoderdata will not affect our algorithm because we only utilizethe path shape within a short distance associated with thetime frame ta−tf .

2) Determination of RRA and RRA#: The results inSection IV-A (refer to Fig. 4) have confirmed that the

overall RRA of an HF RFID reader antenna is closelyrelated to the physical shape of the outer boundary ofthe reader antenna. We therefore approximate the RRAof the TLB reader antenna to be of rectangular shapewith the dimensions W and L that are chosen from thepredetermined parameters stored in the system memory,as mentioned previously in Section IV-A.

Furthermore, the rectangular-shape RRA is bifurcatedinto Zone-1 and Zone-2 by joining its corners diagonallywith an imaginary straight line, as shown in Fig. 7. Thejunction formed between these two zones and the imagi-nary boundaries of the RRA are also shown in the samefigure.

Now, we form a flipped and scaled version of theRRA on the same plane, which we denote as RRA#, asdepicted in Fig. 7. A scaling factor ρ is used to create thisnew scaled version. We now utilize the boundaries of thisnew RRA# along with the intersection points (Pa, Pc,d,and Pf ) that lie on the path of the object (POi) to obtainthe TLB correction factors rβ and θβ .

The scaling factor ρ is determined as follows: Referringto Fig. 7, at point Pf , the POi can be considered tobe tangential to the XTLB-axis and perpendicular to theboundary of RRA#. Using these geometrical parametersand considering that the plane of the RRA# lies parallelto the X−Y plane, a unit vector rws

can be obtained.In a similar manner, another unit vector rfa along lafcan also be obtained. Then, the angle θaf and the scalefactor ρ are determined using the following expressions:θaf = cos−1(rws

· rfa), and ρ = L/(laf sin(θaf ).3) Finding TLB correction factors by fitting the intersec-

tion points to the RRA#: The aim here is to obtain thebridge correction factor (rβ and θβ) by fitting the pointsof intersection (Pa, Pc,d, and Pf ) to the boundaries ofRRA#. We will employ a geometric approach in orderto solve this problem. Referring to Fig. 7, the bridgecorrection parameters can be determined by

rβ =

√l2x +

(L

2

)2

(3)

θβ = tan−1

(lx(L2

))

(4)

where

lx =

(ldf sin(θd)

sin(αc)

)ρ− W

2

θd =π − (ϕc + ϕf ) αc = tan−1

(L

W

)

ϕc =

{αc − θaf , for θdf ≥ θafπ − (αc + θaf ), for θdf < θaf

,

ϕf = cos−1

(l2af + l2df − l2ac

(2laf ldf )

)θdf = cos−1(rws

· rfd).

4) Find the position of the object: Having calculated the BPcorrection factors, we will now obtain the relative position


Fig. 8. Estimation of orientation angle using tag positions along the traveledpath using the TLB reader antenna.

of the object with respect to the detected tag as

PRel_Tagi=

[rβ cos(θk + θβ)

rβ sin(θk + θβ)

]Tagi

. (5)

The term θk is the heading of the object relative to theXTag-axis. Finally, the object position PTLB(t) at time“tf” is estimated using both the relative position of theobject PRel_Tagi

and the coordinates of the detected tagPTagi

as

PTLB(t) =

[xTagi

+ rβ cos(θk + θβ)

yTagi+ rβ sin(θk + θβ)

]. (6)

Next, we discuss methods to obtain object orientation θk.

D. Estimation of the Object Orientation

The object orientation θk is defined as the angle between theXTLB- and XTag-axes, as shown in Fig. 8. The object orien-tation can be estimated using current and previous estimatedpositions as described in [1], [6], [15]. However, this methodsuffers from position uncertainty caused by the RRA, whichleads to errors in the estimated object orientation. Since theproposed TLB antenna can reduce the uncertainty caused byRRA, it is worthwhile investigating whether it can also help toimprove the estimation of orientation.

The orientation estimation can be obtained by utilizing theinformation from the TLB reader antenna and the path traveledbetween previous and current detected tags. This algorithmdoes not rely on exact path as it only requires information onthe relative positions of the object within the path.

First, we obtain the relative locations of the object withrespect to the path traveled between two consecutive tags (forexample, Tag A and Tag B) using the Mode-2 positioning

algorithm. Consider a scenario as illustrated in Fig. 8, wherewe can estimate the current orientation of the object using

θk = θAB + θFB. (7)

θAB is the angle between the slope of the line connecting thecurrent and previous detected tags to the XTag-axis, calculatedusing

θAB = atan2(ytagB

− ytagA, xtagA

− xtagB

). (8)

In (8), the detected tag positions are retrieved from the RFIDreader database. The θFB

in (7) is the angle between the currentobject heading vector �rFB

and another vector �rAB linking pre-vious and current estimated tag positions. The θFB

is obtainedusing

θFB= ± cos−1 (rAB · rFB

) . (9)

The rAB and rFBare the unit vectors of �rAB and �rFB

, respec-tively. These vectors are obtained from

�rAB =PTagB_Path (tfB )− PTagA_Path (tfA) (10)�rFB

=PPath (t+fB )− PPath (t−fB ) (11)

where

PTagi_Path =PPath (tfi) +

[rβi

cos (θk_Pathi+ θβi

)

rβisin (θk_Pathi

+ θβi)

]θk_Pathi

= atan2((yPathi

(tf )− yPathi−1(tf )

xPathi(tf )− xPathi−1

(tf ))

i ∈{A,B, . . .}.

The PTagi_Path(ti) in (10) represents the coordinates of tagiat time ti, and in (11), the PPath(ti) indicates the coordinatesof any point on the path, as illustrated in Fig. 8. These coordi-nates (PTagi_Path(ti) and PPath(ti)) are relative to a referencepoint that can be chosen anywhere closer to the path. Thus,the reference point can be independent. In other words, theactual coordinates for (PTagi_Path(ti) and PPath(ti)) are notnecessarily known. Hence, the accumulated errors that mightoccur in the encoder data prior to the estimation of the previoustag position do not have any effect on the accuracy of thealgorithm used for orientation estimation.

E. Overall Positioning Algorithm

The overall sequence of our proposed positioning algorithmis illustrated in the flow chart presented in Fig. 9. The proposedalgorithm allows any moving object to be localized for bothdense and sparse arrangements of tags on a grid. The algorithmis mainly optimized for sparse tag-grid floor infrastructureas it helps to achieve cost effectiveness of installation andmakes the deployment flexible enough to suit any applicationenvironments or infrastructure scenarios.

In the event that no tag gets detected, the system derivesposition using only the wheel encoder data with its referencetaken from the most recent RFID measurement. This approachreduces potential errors that could be caused due to any accu-mulated offset or drift in the encoder data. In addition, it alsoensures continuity of positioning.


Fig. 9. Flow diagram of RFID-based positioning using triangular bridge-loopantenna.

This overall positioning algorithm is utilized in both Matlabsimulations and experimentation. The results obtained from theMatlab simulations are discussed in Section V-F, and the resultsfrom the experimentation are presented in Section VII. Bothresults demonstrate the efficacy of our proposed approach.

F. Error Comparison for Different Tag-Grid Separations

Here, we make a comparison of positioning performancesbetween a system that uses a conventional loop antenna (de-noted as Sys-A) and the one that uses the proposed TLBreader antenna (denoted as Sys-B-M1 when using Mode-1 andSys-B-M2 when using Mode-2). For this comparison, we as-sume that, for all the systems, the orientation of the object atany instance is known.

The floor-tag grid is arranged in a triangular pattern, asillustrated in Fig. 10. This arrangement is suitable to reducethe number of floor tags [15]. Tag grid is considered to besparse when only one tag can be detected at any time instant.It is the case when the parameter htag, as shown in Fig. 10,becomes larger than the width “W ” of the RRA of the readerantenna. When the tag grid is dense (i.e., when two or more tags

Fig. 10. Tag floor and the desired path.

can be detected), the system averages the coordinates of all thedetected tags to estimate the current position.

Under sparse tag arrangement, i.e., when only a single taggets detected, the systems employing Mode-1 (Sys-B-M1)and Mode-2 (Sys-B-M2) use algorithms that are described inSection V-B and V-C, respectively. To make a proper compari-son, we simulate using Matlab the movement of an autonomousobject that is equipped with HF RFID reader to follow a definedpath, as shown in Fig. 10. Estimations are repeated for differenttag-grid separations by increasing the intertag separation dis-tance “dtag.” Average position errors are computed using

Average Position Error =1

T

T∑t

e(t) (12)

where “t” is the time instance at which the position estimation ismade and “T ” is the total number of position estimations madewhile the object traverses along the chosen path completely toreach the final destination. The term e(t) is a position errorbetween an actual position X and an estimated position Xcomputed as e(t) =

√(x− x)2 + (y − y)2.

The average positioning error plotted in Fig. 11 clearlydemonstrates that the TLB reader antenna outperforms theconventional reader antennas, particularly for larger intertagdistances, i.e., when the tag grid is sparse.

If conventional reader antennas are used in this scenario, itcan lead to larger positioning errors since the intertag distancedtag tends to be larger than the dimensions of the RRA,resulting in the reduction of the number of tags detectableby the reader. It must be noted that the conventional readersrequire detection of multiple tags to obtain the position in-formation. However, in the limiting case when just only onetag is detectable due to larger tag separation, the positioningerrors deteriorate further. Thus, when separation dtag > r, anerror threshold will be reached, and as shown in Fig. 11, itoccurs for an intertag separation distance of 19 cm [for thedefinition of “r,” refer to Fig. 5(a)]. Any further increase in theintertag distance of the tag grid will further reduce the chancesof detecting multiple tags, thus severely limiting the positioningperformance when employing conventional reader antennas.However, in such scenarios, those systems that use the proposed


Fig. 11. Comparison of positioning errors using conventional reader antennaversus the proposed triangular bridge-loop reader antenna.

TLB antenna (Sys-B-M1 and Sys-B-M2) start to perform better.The errors obtained by Sys-B-M1 are lower than those for theconventional system (Sys-A) for larger intertag distances dueto the reasons explained previously. For a tag separation dtagequal to 36 cm, the error due to Sys-B-M1 becomes stable withthe maximum error falling slightly below 8 cm.

For Sys-B-M2, its average position errors start to reducegradually at a tag separation of around 23 cm. This occurs dueto the utilization of the wheel encoder data in addition to the BPsignal. However, when the intertag distance becomes equal to36 cm, i.e., when htag > W , the errors tend to become lower,i.e., they fall below 2 cm. As noted previously, beyond a tagseparation of 36 cm, the reader can detect only a single tag.

The dimensions of reader antenna play a significant role asthey determine the required tag infrastructure and the level ofaccuracy. The dimensions of the antenna must not be chosen tobe too small to avoid null tag detection throughout the localiza-tion process because it reduces the chances of reading a tag.

The aforementioned results confirm the efficacy of our pro-posed method involving the TLB antenna for sparse floor-tag-grid infrastructure.

VI. EXPERIMENTS

To further validate our claims on the superior performanceof the proposed TLB antenna and positioning algorithms, weperformed a series of experiments. Our experiments involvedlocalization of an autonomous wheelchair inside a multistorybuilding, and the results are used for comparison with simula-tions. First, we compare the results obtained using the proposedTLB reader antenna with those obtained from a conventionalloop reader antenna. Second, we compare our approach withthe recently published results given in [10] and [6].

The measurement setup used in our experimentations isshown in Fig. 12. Passive tags are sparsely arranged on thefloor with a tag separation of about 1.3 m. The reader antennais mounted at the base of the moving autonomous wheelchair.

Fig. 12. (a) RFID-reader-based positioning for an autonomous wheelchair.(b) Proposed TLB reader antenna placed at the bottom of the wheelchair.

For a fair comparison, the TLB antenna is chosen to havethe same outer dimensions (230 mm × 320 mm) as that of acommercially available conventional loop reader antenna. Thisis to ensure comparable-size RRAs from both antennas.

The wheelchair is moved on the floor to follow a prescribedpath, as depicted in Fig. 12(a). The speed of the object is setto be consistent around 16.6 cm/s so as to ensure that the tagscan be successfully read by the reader. A faster reader and tagcan be used if faster speed is desired without any modificationto the proposed algorithm. Measurements from HF RFID readerdatabase, the BP, and the wheel encoder data are acquired andfed as inputs to our localization algorithm that is described inSection V-E. In particular, the wheelchair position and orien-tation are estimated using (6) and (7), respectively, when thesystem employs the proposed TLB reader antenna.

For simulating the errors caused due to a conventional loopantenna, the position estimation was made using the algorithmgiven in [15], and the orientation is estimated following thealgorithm given in [1]. The positioning error is calculated usingthe procedure given in Section V-F. The orientation error θerroris calculated as

θerror = |θ − θ| (13)

where θ and θ are the actual and the estimated heading angles,respectively. The average of heading angle is calculated using

Average Orientation Error =1

T

T∑t

θerror(t) (14)

where the terms t and T are the same as those defined inSection V-F.


Fig. 13. Performance comparison between the proposed method versus theconventional method. (a) Positioning error. (b) Orientation error.

TABLE ICOMPARISON OF AVERAGE ERRORS

VII. RESULTS AND DISCUSSION

Error comparisons for position and orientation estimates areplotted in Fig. 13(a) and (b). The results in Fig. 13(a) indicatethat the proposed method (TLB antenna plus the algorithm)obtains smaller errors over the period of the localization ascompared to the use of conventional reader antenna and tradi-tional positioning methods. Also, the proposed method obtainsan average positioning error of 4.05 cm, as opposed to anaverage positioning error of 12.41 cm for a system that employsa conventional reader antenna.

A. Performance Comparison: Proposed Reader AntennaVersus Conventional Loop Reader Antenna

Our proposed algorithm for orientation estimation also offersrelatively smaller errors when compared to the conventionalapproach [1]. The proposed method can perform relatively welleven at critical points, i.e., when the object to be localizedmakes a turn, as indicated in Fig. 13(b). On average, our ori-entation algorithm gives an error of 4.51◦ as compared with the14.80◦ error obtained by the available method of orientation es-timation. A comparison of average errors is tabulated in Table I.

TABLE IICOMPARISON OF RFID-BASED POSITIONING METHODS

There are slight differences that can be observed between oursimulation results and measured data. This can be attributablemainly to the chosen rectangular-shape boundary for RRA inour algorithm. This choice was made to make the algorithmsimple while closely reflecting the reality. However, the actualRRA boundary cannot exactly be rectangular and could getmodified either due to the variation of tag sensitivity or dueto the presence of metallic structures that may be presentunderneath the floor and/or close to the tag. Since some of thesefactors are out of our control when performing measurementsin an existing building, they could have influenced the overallaccuracy of our measured data. Methods to overcome theselimitations will be addressed in the future.

B. Comparison With the Recent Methods Publishedin the Literature

Here, we compare the performance of our proposed systemwith the recently published results given in [6] and [10]. Foreffective comparison, we use antenna radius parameter of theproposed systems to indicate improvement over the maximumerror (MaxError). The maximum error is obtained from theradius of recognition area. Also, for fairer comparison, wehave considered cases that use only sparse tag arrangementwhere, on average, only one tag is detected by the reader.Error improvement is calculated using (15). The comparisonis tabulated in Table II

Improvement=

(MaxError−AverageError

MaxError

)× 100%. (15)

The method published in [6] reports an improvement of 52%,whereas the method given in [16], which was also repeatedin [10] with sparse floor-tag grid (dtag = 30 cm), reports animprovement of 65%. Our results consistently demonstrate thatour proposed method outperforms both published approachesby offering improvement of around 79%. In addition, ourmethod employs tags with much higher intertag distances ofabout 130 cm, making the tag grid truly sparse when comparedto reported 34 and 50 cm in the literature [6]–[10].

VIII. CONCLUSION

We have proposed a novel triangular-bridge-loop readerantenna for positioning and presented a method to improveHF-RFID-based positioning under sparse floor-tag-grid


infrastructure. Our proposed reader antenna provides BP as afunction of the tag’s location with respect to RRA. We have alsoproposed a positioning algorithm which gainfully employs theBP to estimate the position and orientation of a moving object.The proposed system allows sparse floor-tag infrastructure,leading to lower cost and flexible tag deployment that can adaptto any applications or infrastructure scenarios. Simulationsand experimental results show improvement in positioningaccuracy even for large tag separations that make the tag gridhighly sparse. Our studies also indicate that, for HF-RFID-based positioning, larger recognition area may not necessarilycause higher uncertainties. The novel bridge-loop concept canalso be extended for many other RFID applications, includingthose that employ dense tag-grid infrastructure.

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Mohd Yazed Ahmad received the B.E. degree fromthe Department of Electrical Engineering, Universityof Malaya, Kuala Lumpur, Malaysia, in 2003 andthe M.S. degree from the Department of BiomedicalEngineering, Faculty of Engineering, University ofMalaya, in 2006. He is currently working toward thePh.D. degree in the Center for Health Technologies,School of Electrical, Mechanical and MechatronicSystems, Faculty of Engineering and Informa-tion Technology, University of Technology, Sydney,Australia.

He is also with the Department of Biomedical Engineering, Faculty of Engi-neering, University of Malaya. His current research interests include localiza-tion, positioning, wireless sensors, data fusion, optimization, instrumentationsystems using RF, and embedded systems.

Ananda Sanagavarapu Mohan (aka A.S. Mohan)(M’92–SM’05) received the Ph.D. degree in elec-trical communication engineering from the IndianInstitute of Technology, Kharagpur, India, in 1986.

He is currently with the Faculty of Engineeringand Information Technology, University of Tech-nology, Sydney (UTS), Australia, where he leadsresearch on applied electromagnetics, antennas forbiomedical applications, computational methods forwave propagation, wireless localization and beam-forming techniques, and mobile communications. He

is a core member of the Key University (interdisciplinary) Research Centerfor Health Technologies, UTS. He is an Editor of the International Journal ofAntennas and Propagation.

Prof. Mohan was a corecipient of the Priestly Memorial Award from theInstitute of Radio and Electronic Engineers, Australia. He was the Past Chairof the New South Wales Antennas and Propagation/Microwave Theory andTechniques (IEEE NSW AP/MTT) joint chapter. He has won a number ofcompetitive research grants from the Australian Research Council, NationalHealth and Medical Research Council, and industry.

novel bridge-loop reader for positioning with hf rfid under sparse tag grid

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