Localization in Industrial Halls via Ultra-WidebandSignals

Lukasz Zwirello1, Malgorzata Janson1, Christian Ascher2, Ulrich Schwesinger1,2,Gert F. Trommer2 and Thomas Zwick1

1Institut fur Hochfrequenztechnik und Elektronik2Institut fur Theoretische Elektrotechnik und Systemoptimierung

Karlsruher Institut fur Technologie (KIT), Kaiserstr. 12, 76131 Karlsruhe, GermanyEmail: lukasz.zwirello@kit.edu

Abstract—In this work a study on an indoor ultra-wideband(UWB) localization system for applications in industrial buildingsis presented. Industrial environments are known for beeingextremely difficult in terms of wireless communication, mainlydue to the fact, that this sort of channels are characterized by highconcentration of metal objects, which cause reflections, leadingto strong multipath propagation. A three-dimensional modelof the warehouse has been created and used for deterministicwave propagation simulations. In the simulation, the transmittertravels along a predefined route. The receiver infrastructureconsists of eight antennas with known coordinates. By meansof multiple simulations, the channel influence on the UWBsignals has been determined. The implications of this influence,in terms of practical system design and localization accuracy, areassessed. Furthermore the influence of pulse detection methodsand geometrical configurations of base-stations are investigated.The position calculation of the mobile beacon is realized usingTime Difference of Arrival approach (TDoA), employing a directsolution as well as iterative methods. Eventually the accuracy ofobtained results and the theoretical limit are considered basedon dilution of precision (DOP) evaluations.

I. INTRODUCTION

In recent years the ultra-wideband (UWB) technologygained a lot of attention in the world of wireless engineer-ing. The reason for this is twofold: First, due to the largeoccupied bandwidth, UWB disposes of potential capability tohandle high data-rates, which is of interest for communicationpurposes. Second, the short pulses used in IR-UWB pledge thehigh time resolution, that should allow precise range estimatesin localization systems. Further very important property ofshort pulses is their multipath immunity. Like this, the reliablesignal transmission, in environments that suffer from heavymultipath effects, can now be realized.The one of the possible fields of application, that can con-siderably profit from the unique properties of ultra-widebandsignals, is the wireless communication in industrial buildings.

As yet, the wireless technology couldn’t win through inthis kind of facilities, mostly because of very tough propa-gation conditions for electro-magnetic waves. The presenceof big and highly reflective objects (mostly metal) and theirgeometrical collocation leads to severe multipath propagation.In this sort of environment the impulse based UWB (IR-UWB) could be potentially applied for wireless controlling of

robots, tracking of workers or even used for steering driverlesstransportation systems.

The aim of this work is, firstly to gain the impression ofsignal distortion in this kind of environment and its impacton the systems parameters like achievable range and updaterate and secondly, to investigate the positioning accuracy independance on kind of employed pulse detection method andgeometrical configuration of receiver stations.

The paper is structured as follows: in section II the channelmodel of industrial environment together with the wave prop-agation simulation tool are described, section III and IV con-tain respectively the statistical parameters of the propagationchannel and the methods of identifying the non-line-of-sightcases. Applied positioning methods are described in sectionV, in section VI the optimal receiver placement is discussed.Finally the simulation results are presented in section VII andin section VIII the work is summarized and concluded.

II. AN INDUSTRIAL ENVIRONMENT SCENARIO

The assembly hall of the Institut fur Produktionstechnik atthe Karlsruhe Institute of Technology was chosen to serve asa reference for the simulation of the UWB localization systemand in future will be used for verification by measurement. Thedimensions of the building, as well as the inside facilities, weremeasured and the exact model in 1:1 scale was implementedfor use with the Ray-Tracing tool. The photograph of the realfacility and the model built for the simulation purposes can beseen in Fig. 1.

3D Ray-Tracing finds and calculates all possible propaga-tion paths between transmitter and receiver, by means of quasi-optical wave propagation in a three-dimensional environment.The individual paths are characterized by frequency dependentattenuation, delay and phase shift and eventually are addedcoherently at the receiver, to determine the complex fieldstrength. In the simulation the propagation phenomena likereflection, scattering, and diffraction are taken into account[1], [2].The measured antenna characteristics can be included inthe simulation, however in this work isotropic antennas areassumed.From its nature, the Ray-Tracing is a narrow-band simulation

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technique, but it can be extended for the UWB purposes, asdescribed in [3].

For localization purposes a total number of 8 receivers inthe building is used. The six outer receivers are located inone plane (in 8 meter height), on the outline of the rectanglein close proximity to walls; the other two are located in themiddle, one meter above the others, as marked in Fig. 7 (bluestars).In this scenario a 58.4 m long route (584 points in 10 cmseparations) of the moving transmitter has been simulated. Inthis particular simulation the beacon moves along the trackmaintaining the constant 1.5 m height above the ground forthe whole time. The route, represented as a red line, can beseen in Fig. 1 (down).

Fig. 1. Photograph of a general purpose assembly hall at the Institut furProduktionstechnik (up) and its 3D model used for Ray-Tracing simulations(down). Shapes marked in black are industrial machines; red line representsthe transmitter route.

III. CHANNEL CHARACTERIZATION

For determination of all important operational parameters ofthe future localization system, the environment, in which it willbe deployed, has to be fully characterized. The properties likecoverage of the system or the position update rate are directlycoupled with channel parameters like attenuation or root-mean-square delay spread. In order to determine a statisticalrange of parameters of a multipath channel, simulations atmany various locations are required. For this purpose the sam-pling grid has been created, in which the transmitter positionhas been varied in x and y direction in steps of 2 metersand constant height of 1.5 meter. Like this, the transmissionbetween each TR-pair (transmitter-receiver) is obtained. Forthis scenario, the total number of different positions is around100. This value, if multiplied by the number of employed

receivers, gives the total of 800 simulated channels, fromwhich the statistic can be generated.In the following subsections the characteristic values of an in-dustrial environment, will be calculated and briefly described.

A. Frequency domainThe channel transfer function (H(t, f)) was simulated in

a frequency range between 2.5 and 11.2 GHz with dis-crete frequency increment fstep of 3.125 MHz. The simulatedspectrum includes the specified by the FCC [4] range offrequencies dedicated for unlicensed usage. The frequencystep determinate the unambiguous range, which in this case is320 ns (τunamb = 1/fstep). This unambiguous range assuresthat propagation paths of the signals with a total length up to96 meters will be resolved. All signals that have traveled thedistance that is longer than the unambiguous one can be safelyneglected due to high attenuation.The parameter that can be directly extracted from the transferfunction is the attenuation of the signal. For large numberof measured channels the most suitable representation can beachieved with cumulative distribution function (CDF). Thisis done for each receiver, and finally the average from allreceivers is built. The resulting CDF of the mean channelattenuation for all TR-configurations is presented in Fig 2.

Fig. 2. Cumulative distribution function of the mean signal attenuation -average curve from all receivers.

In approximately 85% of all situations the attenuation isaround 80 dB or less. This information is a very importantparameter in the process of evaluating the range of positioning,or in general any communication system. To obtain full infor-mation, the specification of the used transmitter and receiverhas to be known. The parameters like peak voltage of thetransmitted pulse, that can be increased at the cost of smallerpulse repetition frequency (data rate) to improve the signal tonoise ratio (SNR), or sensitivity of the receiver are needed toobtain full information about the maximal range.In case the used hardware does not allow to change thementioned parameters it is possible to subdivide the wholelocalization system in smaller units.

B. Time domainThe channel behavior in time domain can be analyzed by

means of channel impulse response (h(t, τ)), further referred

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to as CIR. It can be obtained by inverse Fourier-Transformof H(t, f).In time domain the parameter of interest is the delay spread,which is a measure of multipath richness of the channel.Delay spread evaluates the time difference in arrival timeof the first and last multipath component in the impulseresponse. By setting the pulse repetition rate in the system tothe value larger than the calculated delay spread, it can beassured that no inter-pulse-interference (IPI) will occur. Thedelay spread is calculated with the following formula:

τDS =

√√√√√√√√+∞∫−∞

τ2 · PDP (τ)dτ

+∞∫−∞

PDP (τ)dτ

−

+∞∫−∞

τ · PDP (τ)dτ

+∞∫−∞

PDP (τ) dτ

2

(1)

where the PDP stands for Power Delay Profile and is definedas PDP (t, τ) = k|h(t, τ)|2. Factor k relates the total trans-mitted power contained in the sent pulse, to the total receivedpower in the PDP. In (1) the time dependency of τDS was notconsidered for simplification.The CDF of all delay spreads, calculated for a given proba-bility, is presented in Fig 3.

Fig. 3. Cumulative distribution function of the mean excess delay - averagecurve from all receivers.

The pulse repetition frequency (PRF) of the system isconstrained by the number of IPI, which is allowed in normaloperation mode. For a collision-free transmission with 99%of probability, the PRF in this environment should not exceed20 MHz.

IV. NLOS DETECTION

Knowing the statistical parameter of the channel, the prob-lem of interpretation of received signals has to be addressed.It is obvious, that all localization algorithms can only workproperly, when they are supplied with valid data. In this caseit means that the CIRs coming from NLOS channels have tobe detected and discarded, otherwise the time of arrival ofthe pulse will not correspond to the true spatial separation oftransmitter and receiver.

This recognition can be done by evaluating the signal at-tenuation for each TR-pair. In Fig. 4 the mean path lossalong the route for the receiver number 2, is shown. In threeregions a rapid and significant reduction in receive powercan be observed. This is due to transitions between LOS andNLOS situations. The decision about presence of NLOS canbe done, if the attenuation is larger than 77 dB (limit for thehighest expected attenuation in this geometric set-up for theunobstructed propagation).Where in Wireless LAN (IEEE 802.11) systems the signalstrength measurements (RSSI) can be carried out relativelyeasily, in IR-UWB this kind of measurements would requiresophisticated and costly hardware. For this reason it is of in-terest to use time domain techniques for NLOS detection, thatdon’t rely directly on the evaluation of the pulse amplitude.

Fig. 4. The mean signal loss at receiver nr. 2, for all positions along theroute.

The time domain method bases upon observation of thechange in delay of the first path, in respect to previousmeasurements. This approach has been proposed in [6]. InLOS propagation scenarios, in most cases, the first incomingsignal at the receiver site is at the same time the strongestone. From the simulated data, the plot representing the timeof arrival of the strongest received pulse can be extracted andis presented at the top of Fig. 5. In LOS regions this curvechanges slowly and is rather smooth as the transmitter movesalong the track, whereby in NLOS regions the curve showsvery abrupt changes in measured distance and in majority ofcases indicates much larger distances. By building the deriva-tive of this curve, the middle plot form Fig. 5 is obtained. Inorder to make a decision based on this curve, an additionalboundary condition, regarding maximal allowed velocity, isrequired. This condition is based on a priori knowledge, thatthe transmitter cannot move by more than a certain distancebetween two consecutive measurements, made in a given timeinterval. Having in mind the separation of simulation pointsof 10 cm the maximum allowed displacement is amply set to1 m. By applying this constraint, the bottom plot in Fig. 5 isobtained. The positions at which the NLOS was detected areset to zero.Similar considerations can be conducted for all of the receiversused in the system.

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Fig. 5. TR-distance calculated from the arrival time of first path component(up). Velocity during position change, obtained by differentiation of distanceinformation (middle). Corrected distance information after NLOS-case recog-nition (bottom).

V. POSITIONING

In each CIR, that has been classified as LOS case, thesearch for the first impulse, representing the direct path, isperformed. Having the information about the time of arrival,the time differences between the receivers can be calculatedand used as input for a Time Difference of Arrival (TDoA)localization algorithm. This is a simple approach since onlysynchronization between the receivers has to be performed.The localization algorithm is composed of two steps - first thestarting point for the optimization is picked using the algorithmfrom Bancroft [5]. Then further search is performed withan iterative method. For both methods, the following rangedifference equation holds for each time difference with ~rE1 asa base receiver and the transmitter position ~rS :

c ·∆t1,i = ‖~rS − ~rE1‖ − ‖~rS − ~rEi‖ (2)

As receivers and transmitter are quite close to each other,the error surface of the resulting equation system is rough,especially with noise. Therefore it is crucial to find a goodstarting point for an iterative method. The Bancroft algorithmmakes it possible to transform the nonlinear problem into alinear algebra problem and to solve directly for the unknowncoordinates.It was determined, that using an iterative method with acalculated starting point from the Bancroft algorithm yieldsbetter positioning results. Regarding different iterative meth-ods, there is a tradeoff between accuracy and complexity, asthe computation time will rise for more refined algorithms.Performed comparisons shown, that a standard Levenberg-Marquardt Algorithm is robust and delivers a good positioning

accuracy. To consider position boundaries, an algorithm, whichuses active-set constraints to avoid position solutions outsidethe assembly hall, was also examined. Simulation results show,that with application of realistic pulse detection algorithms, butno added noise, a positioning improvement of more than 15%after refining the Bancroft algorithm solution with the iterativemethod is obtained.In order to investigate the speed of the algorithms and withthis, their capabilities to handle real-time localization, 1000simulations were performed. The direct solution with Bancroftalgorithm took in average less than 5 ms, whereas Levenberg-Marquardt-Algorithm needed 50 ms in average. Tests con-ducted on a standard PC with 2 GHz single-core processor.Like this both used algorithms can handle the task of real-time operation and depending on requirements either one ofthem or both can be used.

VI. OPTIMAL RECEIVER CONSTELLATION

To get an optimal constellation of receivers, it is importantto know the value of the positioning Dilution of Precision(PDOP), which describes the expected degradation of the posi-tion accuracy in the current transmitter-receiver-constellation.

For uncorrelated measurements with equal noise variancevalues, the PDOP is defined as

PDOP =√QXX +QY Y +QZZ (3)

where QXX , QY Y and QZZ are the diagonal elements of thematrix Q, which is defined to

Q = (HT ·H)−1 (4)

and matrix H is the measurement matrix of the linearizedequation system from equation 2.

When using time differences, it is necessary to decorrelatethe measurements, because the differences are all calculatedto one base receiver. So all difference measurements will becorrelated with the noise of that base receiver. Assuming equalnoise σt for all time measurements, the resulting covariancematrix is fully occupied:

R∆t =

2σ2

t σ2t . . . σ2

t

σ2t 2σ2

t . . . σ2t

......

. . ....

σ2t σ2

t . . . 2σ2t

(5)

If this is ignored, varying DOP values will be achieved fordifferent base receivers without changing the constellation.The decorrelation, also used in [8], can be done with theCholesky decomposition of the covariance matrix R with

R∆t = U D UT (6)

and the decorrelated measurement matrix can be calculatedwith

H∗ = U−1 ·H (7)

The decorrelated covariance matrix D contains only non-equaldiagonal elements. Therefore equation 4 has to be adapted:

Q = (H∗T ·D−1 ·H∗)−1 (8)

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and the PDOP can be calculated using equation 3. The corre-lation has also to be considered in the positioning algorithms.With the PDOP value, one can directly define the expectedpositioning precision σpos3D for a given ranging standarddeviation σt with

σpos3D = PDOP · σt (9)

For the application described in this paper, the Position Dilu-tion of Precision (PDOP) for the used receiver constellation,was studied. The result is presented in Fig. 6. It was observed,that the PDOP values inside the receiver constellation areacceptable but a strong degradation can be found outside thesetup. This is due to the fact, that a position change causesless change in time differences.

Fig. 6. Distribution of PDOP values in the assembly hall with 8 receivers(blue stars). The transmitter route is marked with black line.

VII. RESULTS

Before presenting of the final results, a reference simulationis needed to determine the accuracy for the best case scenario.For this purpose the earlier mentioned route was simulatedin the same building (walls, roof and floor), but without anyobstacles.To get a theoretical estimation of the upper positioningerror bound along the whole trajectory, an average PDOP iscalculated. This value multiplied with the uncertainty of thetime measurements σt and multiplied by 3 yields 3σ boundaryof the resulting positioning error, see also equation 9.σt is defined as the standard deviation of the differencebetween detected ToA of the signal and the predicted ToA(calculated from the geometrical data - ideal one), multipliedby the propagation speed of an electromagnetic wave inthe air. Two different peak search algorithms were tested; a

threshold based approach and an absolute maximum search.

• Maximum search method The time stamp of the pulsepeak is evaluated. The standard deviation of the rangemeasurements with this detection algorithm was foundto be 0.41 cm and multiplied with an average PDOP of2.82 leads to the theoretical 3σ boundary along the wholeroute of 3.47 cm.

• Threshold method To investigate the influence of thresh-old detection, the time measurements obtained with thismethod were evaluated. The scenario from the previousexample is used. Here, the standard deviation of the rangemeasurements with this detection algorithm was found tobe 2.27 cm. With the PDOP from above the theoretical3σ boundary along the whole route is 19.2 cm.

It can be seen, that the threshold detection method leads toan approximately 5-times higher uncertainty in determinationof time of arrival of first pulses and therefore a degradationof positioning accuracy.

In the assembly hall scenario without obstacles, bothpulse detection methods were simulated. For the Maximumsearch method, the mean positioning error obtained by thedirect (Bancroft) and iterative (Levenberg-Marquardt) solutionis 1.13 cm and 1.02 cm respectively (the mean positioningerror is defined as the mean Euclidean distance betweencalculated and the true position).When using the Threshold method, the direct and iterativesolutions lead to errors of 10.43 cm and 10.09 cm respectively.The important effect, that leads to this error, is that dependingon pulse amplitude, the same threshold will cause differenttime readings, although the peak has the same position. Thiscan cause an uncertainty of up tp half the pulse width.

For the complete scenario including all obstacles and againusing the Threshold method, the obtained solution containsaverage errors of 1.131 m (direct) and 1.101 m (iterative). Thegraphical representation of the results can be seen in Fig. 7.

The major contribution to this error, higher than expected,has the inaccurate estimation of several positions at the endof the route (left region in Fig. 7). This is due to the incorrectrecognition of LOS and NLOS cases. The reason for thisis, that the applied method of recognizing rapid changes intime of arrival of the first path is only useful if the NLOSsituation last relatively short to the LOS situations. Otherwise,the biased travel time will be present for a long period andtherefore not lead to detectable distance fluctuations (compareapproximately the last 50 positions in Fig. 5).

After performing the corrections in the decision aboutNLOS at the end of the route, the data set was calculatedonce more. For this case the expected theoretical error for thereduced receiver constellation is 18.8 cm. Then the calculatedsolutions are in average worse by 7.26 and 7.28 cm. In Fig. 8the positioning error along the route, together with correspond-ing limits, is shown. It can be observed, that the major part of

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Fig. 7. Transmitter route and position estimates including scenario obstacles(top view).

the overall error comes from the height component, it can beseen that the height errors are not unbiased. This is due to thetime measurement errors introduced by the Threshold method.Further investigation has to be done on this subject.

Fig. 8. Positioning errors in x, y and z-direction (blue) together with oneand 3σ-limits (green and red).

The inconvenience caused by time offsets could be over-come by implementing a Receiver Autonomous IntegrityMonitoring (RAIM). In cases where more than 5 receiversare available, this algorithm can identify the receiver thatleads to inconsistent results. This method should lead to

better performance of the system, even if the NLOS-situationdetection was not ideal.

VIII. SUMMARY AND CONCLUSIONS

In this paper a comprehensive study on a UWB localiza-tion system, operating in presence of difficult propagationconditions, has been presented. The channel parameters, con-straining the system performance, have been determined andpossible hardware-level solutions were indicated. A methodfor eliminating non-line-of-sight cases has been proposed.For calculation of the mobile beacon position, two differentmethods were used and considerations with respect to theirimplementation in a real system were done. Furthermore thetheoretical and simulation-based limits for localization preci-sion together with a DOP-based quality-measure for evaluationof the obtained results were presented.Based on multiple simulations, the impact of threshold detec-tion on position estimation errors in comparison to the bestpossible estimation of the peak position was determined.

The major influence on the correct position estimation hasthe proper distinguishing between LOS and NLOS cases andthe placement of base-stations, to assure good PDOP-valuesin the scenario. In prospective research the latter two aspects,as well as the influence of further non-idealities, like pulseshape or thermal noise, will be investigated.

ACKNOWLEDGMENT

The authors would like to thank to Landesstiftung Baden-Wurttemberg for financing the work, under research projectname ”Werkzeuge fur die flexible, adaptive Produktion”, aswell as to Markus Schneider, from the Institut fur Produktion-stechnik, for delivering the data needed for creation of the 3Denvironment model.

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