[[abstract]]The Ultra-Wideband Positioning System service of wireless communication industry has become a popular issue in the recent years. This service can provide an accurate location for many useful functions. The main problem that causes the two major errors are: measurement error, and NLOS (Non-line-of-sight) error. Especially the NLOS errors have the most serious effect on mobile position system. This paper will put a high emphasis on how to mitigate the error caused by NLOS. The simulation results show that the proposed algorithm can reach a high accuracy even in a severe environment.[[conferencetype]]國際[[conferencedate]]20061106~20061109[[conferencelocation]]hangzhou, Chin

Lo, Yu-Chiang

The Ultra-Wideband Positioning System service of wireless communication industry has become a popular issue in the recent years. This service can provide an accurate location for many useful functions. The main problem that causes the two major errors are: measurement error, and NLOS (Non-line-of-sight) error. Especially the NLOS errors have the most serious effect on mobile position system. This paper will put a high emphasis on how to mitigate the error caused by NLOS. The simulation results show that the proposed algorithm can reach a high accuracy even in a severe environment.國際20061106~20061109hangzhou, Chin

Tamkang University Institutional Repository

ICWMMN2006 ProceedingsMitigating NLOS Error for UWB Positioning SystemYu-Chiang Lo, Chien-Ching Chiu and Chung-Hsin HuangElectrical Engineering Department, Tamkang University, Tamsui, Taiwan, Chinae-mail : chiu@ee.tku.edu.twII. The basic modelkeywords: Non-Line-ofSight (NLOS) , UWB positioning,LOS.AbstractThe Ultra-Wideband Positioning System service of wirelesscommunication industry has become a popular issue in therecent years. This service can provide an accurate location formany useful functions. The main problem that causes the twomajor errors are: measurement error, and NLOS(Non-line-of-sight) error. Especially the NLOS errors havethe most serious effect on mobile position system. This paperwill put a high emphasis on how to mitigate the error causedby NLOS. The simulation results show that the proposedalgorithm can reach a high accuracy even in a severeenvironment.I. IntroductionUltra- Wideband position system has become a very hot topicin wireless communication system, because it enables us tolocate immediately where the user is. In present , techniquesof position system are TOA (time of arrival), AOA (angle ofarrival), TOOA (time difference of arrival), and the Hybridtechnique that combines TOA with AOA [1]-[3]. But theaccuracy of these techniques had been greatly biased byNLOS propagation [4]. The NLOS effect is mainly from thediffraction, scattering, and refraction. NLOS error mitigationtechniques have been investigated extensively in the literature[5]-[18]. In order to increase the measurement accuracy, wehave to find out a better algorithm to reduce the NLOS effect.In the UWB position system, TOA and AOA techniquesneed more than two base stations to get a position data, andthe hybrid TOAIAOA technique just needs one base station toget a position data. Therefore, we propose a new dataselection algorithm based on the hybrid TOAIAOAtechnique.In Section 2, we will introduce the basic calculationmodel of TOA. In Section 3, we will present our formulawhich can diminish the errors of NLOS. Then, simulationresults will be shown in Section 4. Finally, some conclusionsare drawn in Section 5.In the mobile positioning process, the measured distancebetween base station (BS) and mobile station (MS) can bewritten as:r =d + r" + r NLOSm m m mrm and d m are the measured distance and the actualdistance from MS to the m1h BS, respectively. r; is them1h measurement error and r:LOS is the NLOS error.Note that the superscript "n" and "NLOS" denote themeasurement error and the error caused by NLOS,respectively.Similarly, the angle of arrival between BS and MSmeasured by each BS can be written as [4]:e = do +en+ eNLOSm 'f/m m mWhere em is the angle of arrival measured by the m" BS.rPm is the actual angle between MS and the m1h BS. e;is the m1h measurement error. eNW S is the NLOS error,mand the range of eNW S is from 0 ~ 27r.mIII. NLOS mitigationSince the algorithm presented in this paper is based on theHybrid positioning technique, we are going to introduce theHybrid (TONAOA) positioning technique briefly here.It only takes one base station to calculate a positioningresult by the Hybrid positioning technique. First, we can getthe distance d and the angle e from the measurement ofthe BS. By taking the BS and d as the center and the radius,we can draw a circle, and lengthening a line of the angle efrom the base station. The lengthening line will cross a pointwith the circle and the intersection point is the position resultof MS.In the Hybrid positioning technique, every BS can get aposition data. If we want to have an accurate result, we haveto get rid of all possibilities that it might be affected byNLOS effect. The measurement distance and angle will beinfluenced greatly by NLOS effect. In order to mitigate theNLOS effect, we adopt more base stations for one simulation.In a two-dimensional plane, all the position data willdistribute surround the true MS location theoretically. If thebase station is under large NLOS error or large measurementerror, the position data will be far away from the true MSlocation. (P I, P4 and P5 in Fig. I.) In other words, positiondata which are lightly influenced by NLOS or measurementerror should be intensively distributed around the true MSlocation (P2, P3, P6, and P7 in Fig. I.), as shown in Fig.\. Asa result, we shall remove the position data which wereaffected by large errors and only need the intensivelydistributed position data. Then more accurate result can beobtained by only using the intensively distributed positiondata.Fig.I . The diagrammatic sketch and the distribution about MS..The following procedures and assumptions are made todevelop our proposed algorithm:I. We assume " a " BSs in our simulation. In the Hybridtechnique, each BS is used to predict the position of theMS. Let Pm denote the positioning result from them" BS, and m is from I to a. Then, we totallyhave a results. (~ , P2 , P3 ... , Pa ).2. Let d( u ,v) be the distance between P" to Pv ' andu =I ~ a ,v =I ~ a. We calculate d( u ,v) and moreclose the position result is, more small the d(u ,v) willbe.3. Calculating 0" = ~ d .u = I ~ aL..J (n,v)v:::: 14. Sorting 8" according to its value in descending rule.(Example : if 0 3,0 2,0 4,00,,0' , then ~,P2,P4 " "'~)5. Summation the sorted first 3 position data and averagethem as a final position result. (The reasons why we onlyadopt three position data were written below)I. We do not know how many BSs were affected byNLOS, so we have to assume that almost all BSswere effected, which means the LOS position datawould be in quite few amount. In order to higherthe tolerance of NLOS effect, we have to adoptfewer positioning data as possible.2. If we try to find out the true MS position, usingonly one or two reference position data would biasthe position location result and cause larger error.Therefore, using three position location data is ourbest choice.In this procedure, we do not care whether the errorcaused by NLOS and measurement was large or not. Becausewhen all the positioning data are small error, this algorithmwill bring us more accurate results without doing anythingelse.IV. Simulation resultsIn this section, we will test our proposed algorithm onmitigating NLOS error by computer simulation. Severaldifferent situations are considered, and for each situation1000 times simulations are taken. The location of MS israndomly assigned within the coverage of the cell. Theproposed algorithm is also compared with the other threeposition algorithms (LOS Relative and Whole algorithms).First, we simply introduce the LOS and WholeAlgorithms :(l)LOS algorithm:In this algorithm, the Line-of-Sight position data are onlyused to predict the MS location. This algorithm is an idealcase, because we can not know what BS is affected by theNLOS effect. In order to compare with all algorithms, wehave to assign some specific BSs affected by the NLOS effect.This will not affect the accuracy of all algorithms. In oursimulation, we assign BSI, BS3 and BS4 as BSs with LOSeffect.(2)Whole algorithm:All the position data are adopted and without any NLOS errormitigation.(3)Relative algorithm:As reference [19], we take it's concept, in order to comparefairly, reconstruct this algorithm base on Hybrid AOAlTOAtechnique.The main approach we use to judge every formula'sperformances is Root Mean Square Error (RMSE):RMSE = ~(J'; + (J' .~(j2 and (J' 2 are the variance of the X and Y axis IIIx yCartesian coordinates .In our simulation, we assume the measurement error ofthe angle and distance range are I degree and 19cm,respectively. The NLOS angle error is uniformly distributed,and its range is from - J'( to J'(.The average range of NLOSdistance error and standard deviation are 239cm and 148cm,respectively. The error value was from simulated program inL-shape corridor. The simulation environment in a L-shapecorridor, wide 2 meter, height 3 meter, and length 10 meter.Each BS is in the comer of corridor. It takes 6 BSs tosimulate in this cases. The RMSE error of the three cases isshown in Table.l.From this simulation case, we can find that the performanceof the proposed algorithm is sometimes better than theRelative algorithm, and the RMSE error of LOS and theproposed algorithm is quite close.It is obviously that, from the simulation results, theproposed algorithm can be as accurate as LOS algorithm.Also, the proposed algorithm can tolerate high proportion ofNLOS BSs.I lOS.. J'llOrOSE()v,l1:)LERELATM:Fig.2. The simulation resultTable 1. RMSE of simulated eases (meter) .RMSE (em)Whole 1.418ge+003algorithmLine-O f-Sight 58.9142algorithmProposed 9.4010algorithmRelative 10.4826v. ConclusionsThe proposed algorithm gives a high accurate estimation ofmobile location without any prior knowledge about theNLOS information, and the accuracy between proposedalgorithm and LOS algorithm is very close. This algorithmcan be easily extended to get the NLOS probability of eachBS.References[I] J. 1 Caffery, Wireless Location in CDMA Cellular RadioSystems, Kluwer Academic, 2000.[2] lCaffery, Jr. and G Stubler, "Subscriber Location inCDMA Cellular Networks," IEEE Trans. on VehicularTechnology, May. 1998, ppA06-416.[3] lCaffery, Jr. and G Stubler, "Overview ofradiolocation inCDMA cellular systems," IEEE Commun. Mag, Apr.1998, pp.38-44.[4] M. P. Wylie and 1 M. Holtzman, "The non-line-of-sightproblem in mobile location estimation," IEEE Int. Conf.on Universal Personal Commun, Oct. 1996, pp. 827-831.[5] Ding-Bing Lin, Rong-Temg Juang, and Hsin-Piao Lin,"Mobile location estimation and tracking for GSMsystems" Personal, Indoor and Mobile RadioCommunications, 2004. PIMRC 2004. 15th IEEEInternational Symposium on , Sept. 2004 , Volume: 4 5-8[6] Li Xiong, "Selective model to suppress NLOS signals inangle-of-arrival (AOA) location estimation," Personal,Indoor and Mobile Radio Commun, Sept. 1998,ppA61-465.[7]Yangseok Jeong and Heungryeol You,"Calibration ofNLOS error for positioning systems," VehicularTechnology Conference May 2001, pp. 2605-2608,[8] Xin Wang, Zongxin Wang and O'Dea, B, "A TOA-basedlocation algorithm reducing the errors due tonon-line-of-sight (NLOS) propagation," IEEETransactions on Vehicular Technology, Jan. 2003, pp.112-116.[9] Wuk Kim, Jang Gyu Lee, Gyu-In Jee and ByungSooKim, "Direct estimation of NLOS propagation delay formobile station location, " Electronics Letters, Aug. 2002,pp. 1056 -1057.[10] S.-S. Woo, H.-R. You, and J.-S. Koh, "The NLOSmitigation technique for position location using IS-95CDMA networks, " in Proc. IEEE Vehicular TechnologyConference, vol. 4, Sept. 2000, pp. 2556-2560.[11] M. P. Wylie and J. Holtzman , "The non-line of sightproblem in mobile location estimation, " in Proc. IEEEInternational Conference on Universal PersonalCommunications, vol. 2. 1996, pp. 827-831.[12] P.-c. Chen, "A non-line-of-sight error mitigationalgorithm in locationestimation," in Proc. IEEEWireless Communications Networking Conference,vol. 1,1999, pp. 316-320.[13] 1 Borras, P. Hatrack, and N. B. Mandayam, "Decisiontheoretic frameworkfor NLOS identification," in Proc.IEEE Vehicular Technology Conference, vol. 2, 1998, pp.1583-1587.[14] S. Wang and M. Green, "Mobile location method fornon-line-of-sight situation," in Proc. IEEE VehicularTechnology Conference, vol. 2, Sept. 2000 , pp. 608-612.[15] L. Cong and W. Zhuang, "Non-line-of-sight errormitigation in TDOA mobile location," in Proc. IEEEGlobecom, Nov 200 I, pp. 680-684.[16] M. P. Wylie and S. Wang, "Robust range estimation inthe presence of the non-line-of-sight error," in Proc.IEEE Vehicular Technology Conference, vol. 1, Fall.2001, pp. 101-105.[17] S. Venkatraman, 1 Caffery, and H.-R. You, "Locationusing LOS range estimation in NLOS environments," inProc. IEEE Vehicular Technology Conference, vol. 2,Spring. 2002, pp. 856-860.[18] S. Al-Jazzar, J. Caffery, and H.-R. You, "A scatteringmodel based approach to NLOS mitigation in TOAlocation systems, " in Proc. IEEE Vehicular TechnologyConference, vol. 2, Spring. 2002, pp. 861-865.[19] Neiyer S. Correal, Spyros Kyperountas, Qicai Shi andMatt Welborn. "An UWB Relative Location System"2004

Mitigating NLOS error for UWB Positioning System

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Mitigating NLOS error for UWB Positioning System

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