gps multipath mitigation for urban area using omnidirectional infrared camera

22 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 10, NO. 1, MARCH 2009

GPS Multipath Mitigation for Urban Area UsingOmnidirectional Infrared Camera

Jun-ichi Meguro, Taishi Murata, Jun-ichi Takiguchi, Yoshiharu Amano, and Takumi Hashizume

AbstractThis paper describes a precision positioning tech-nique that can be applied to vehicles in urban areas. The proposedtechnique mitigates Global Positioning System (GPS) multipathby means of an omnidirectional infrared (IR) camera that caneliminate the need for invisible satellites [a satellite detected bythe receiver but without line of sight (LOS)] by using IR images.Some simple GPS multipath mitigation techniques, such as theinstallation of antennas away from buildings and using choke ringantennas, are well known. Further, various correlator techniquescan also be employed. However, when a direct signal cannot bereceived by the antenna, these techniques do not provide sat-isfactory results because they presume that the antenna chieflyreceives direct signals. On the other hand, the proposed techniquecan mitigate GPS multipath, even if a direct signal cannot bereceived because it can recognize the surrounding environment bymeans of an omnidirectional IR camera. With the IR camera, thesky appears distinctively dark; this facilitates the detection of theborderline between the sky and the surrounding buildings, whichare captured in white, due to the difference in the atmospherictransmittance rate between visible light and IR rays. Positioningis performed only with visible satellites having fewer multipatherrors and without using invisible satellites. With the proposed sys-tem, static and kinematic evaluations in which invisible satellitesare discriminated through observation using an omnidirectionalIR camera are conducted. Hence, signals are received even ifsatellites are hidden behind buildings; furthermore, the exclusionof satellites having large errors from the positioning computationbecomes possible. The evaluation results confirm the effectivenessof the proposed technique and the feasibility of highly accuratepositioning.

Index TermsGlobal Positioning System (GPS), infrared (IR)image sensors, multipath mitigation, self-positioning, urban areas.

I. INTRODUCTION

CURRENTLY, Global Positioning System (GPS) applica-tions are rapidly gaining popularity. With the plannedGPS modernization program of the U.S., European SatelliteNavigation System (GALILEO) of Europe, the Global Naviga-

Manuscript received August 10, 2007; revised January 14, 2008. Firstpublished February 2, 2009; current version published February 27, 2009. Thiswork was supported in part by the Research Fellowships from the Japan Societyfor the Promotion of Science for Young Scientists 18-467. The Associate Editorfor this paper was N. Zheng.

J. Meguro and T. Murata are with the Graduate School of Science andEngineering, Waseda University, Tokyo 162-0041, Japan (e-mail: [email protected]; [email protected]).

J. Takiguchi is with Kamakura Works, Mitsubishi Electric Corporation,Kamakura 247-8520, Japan, and also with Waseda University, Tokyo 162-0041,Japan (e-mail: [email protected]).

Y. Amano and T. Hashizume are with the Advanced Research Institute forScience and Engineering, Waseda University, Tokyo 169-8555, Japan (e-mail:[email protected]; [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/TITS.2008.2011688

tion Satellite System of Russia, and the launch of quasi-zenithsatellites by Japan, the availability of satellite positioning isanticipated to improve [1], [2]; however, because of the seriousimpact of multipath on the positioning accuracy in urban areas,such improvements in the availability of satellite positioning donot necessarily facilitate high-precision positioning at the sametime [3][5]. In addition, high-accuracy positioning using GPSin urban areas is desired in the field of intelligent transportationsystems (ITS) [5][10]. Mobile mapping systems [10] requirecontinuous accurate self-positioning even near high-rise build-ings, which are particularly responsible for generating multi-path error. Therefore, it is essential to establish a positioningtechnique in the future to sort the available satellites and onlyuse those with the fewest multipath errors [11][26].

In contrast, simple techniques such as the installation ofantennas away from buildings and using choke ring antennasare known to contribute toward multipath mitigation; however,these techniques are either impractical or very limited. Vari-ous correlator and receiver autonomous integrity monitoring(RAIM) [11], [12] techniques can also be used for multipathmitigation, which are highly practical and popular. The narrow-correlator technique [13], which was proposed in the early1990s, can eliminate multipath errors better than the formermethods. In addition, the earlylate slope technique [14] andthe strobe-correlator technique [15], [16] were also developedin the 1990s. Currently, the use of multipath estimating delay-locked loops (MEDLLs) [17], [18] is widespread, and theVision correlator technique [19] is developed. RAIM providesan alert by checking between the position solutions obtained bysatellite signals.

Meanwhile, multipath mitigation techniques to classify in-visible satellite [a satellite detected by the receiver but withoutline of sight (LOS)] or visible satellite came to front. A multi-path simulation method that uses 3-D geographic informationsystems (GIS) [24], which simulate multipath by means ofpremeasuring building height information, has been proposed.An evaluation tool to predict the satellite constellation serviceavailability along a given terrestrial trajectory by means of avisible-light fisheye optic system [26] is proposed. At the sametime, the results obtained by using a positioning technique formultipath mitigation, in which invisible satellites are excludedfrom the positioning computation and the satellite visibilityis determined from the sky projection of the satellites andobstructions such as buildings, prove that it is possible toimprove the positioning accuracy.

This paper, therefore, describes a precision positioning tech-nique that can be applied to vehicles in urban areas, andproposes a technique to realize multipath mitigation by usingan omnidirectional infrared (IR) camera to exclude invisible

1524-9050/$25.00 2009 IEEE

MEGURO et al.: GPS MULTIPATH MITIGATION FOR URBAN AREA USING OMNIDIRECTIONAL INFRARED CAMERA 23

satellites. This technique employs an omnidirectional IR cam-era and a satellite orbit simulator to automatically determine thegeometrical relation between the satellites and the obstructions,as seen from the vehicle; this enables operation with satellitesthat only have small multipath errors by excluding invisiblesatellites from the positioning computation.

II. OUTLINE OF THE PROPOSED TECHNIQUE

To exclude the radio waves emitted from invisible satellites,the satellite positions, the movable bodys position and attitude,and the physical relation with the obstructions blocking theradio waves from the satellites must be identified at all timesto determine the visibility of the satellites. The techniqueproposed herein involves excluding the invisible satellites byobserving the satellite positions with a satellite orbit simulator,the movable bodys heading angle with an angular displacementsensor like a gyro or inertial measurement system (IMU), andthe obstruction positions with an omnidirectional IR camera.The specific algorithm employed in this technique is shown inFig. 1. First, to obtain the elevation and the azimuth anglesof the satellite as seen from the movable body, the positionof the satellite is estimated from the ephemeris data. Then,the satellite position is converted into the elevation and theazimuth angles of the satellite as seen from the movable bodyby using the approximate position of the movable body andthe angular displacement sensor. Here, even if the approximateposition of the movable body is significantly shifted from theactual position, the shift is small in comparison to the distancebetween the satellite and the movable body; thus, the elevationand the azimuth angles are hardly affected and are sufficientfor accurate computation, even for GPS point positioning.The system then proceeds with a simple segmentation of theomnidirectional IR camera image to enable an understandingof the obstruction positions as seen from the movable body.The estimation of time needed to process an image is afew milliseconds. After the obstructions are abstracted away,the omnidirectional IR camera image is set on the plane ofthe elevation and azimuth angles. Then, on the image with theobstructions abstracted away, the satellite positions are plottedto determine the visibility of each satellite from the overlappingof the satellites and the obstructions. Finally, the positioning isperformed only using visible satellites having small multipatherrors and without using the invisible satellites.

III. ACQUISITION OF FAR-IR OMNIDIRECTIONAL IMAGES

A. Developed Omnidirectional Far-IR Camera

The omnidirectional IR camera developed in this paper isshown in Fig. 2. This camera can generate IR images with anelevation of 2070 for the entire surrounding area over 360[28], [29]; it is capable of taking clear images of buildings, evenat night. A two-mirror optic system is adopted because it iseasier to design than wide-angle lens like fisheye in the caseof far IR rays. Fig. 3 shows images simultaneously taken by avisible-light fisheye camera and an omnidirectional IR cameraat the same place during the day as well as at night. With theIR camera, the sky is distinctively dark; this makes it easy to

Fig. 1. Multipath mitigation algorithm using omnidirectional IR camera.

detect the borderline between the sky and the buildings, whichare captured in white, due to the difference in the atmospherictransmittance rate between visible light and IR rays [30]. Fur-thermore, halation of the charge-coupled device (CCD) imagesensor caused by sunlight and street lights is observed in the redcircle in the fisheye camera image in Fig. 3, whereas this is notobserved in the omnidirectional IR camera images. Therefore,using an omnidirectional IR camera enables a robust determina-tion of the borderline between an object and the sky in the pres-ence of outdoor lights and other disturbances; thus, it is possibleto identify the borderline in a reliable manner, even in the caseof image processing using simple binarization (see Table I).

B. Calibration of the Omnidirectional IR CameraIt is essential to determine the accurate intracamera parame-

ters when surveying with the camera; furthermore, it is nec-essary to employ an accurate method of projection. However,


Fig. 2. Omnidirectional IR camera developed in this study. (a) Omnidirectional IR camera. (b) Description of the optic system.

Fig. 3. Comparison between images from the color fisheye camera and the omnidirectional IR camera. (a) Daytime. (b) Nighttime.TABLE I

IR CAMERA CONFIGURATION

Fig. 4. Calibration box for the omnidirectional IR camera.

Fig. 5. Static evaluation test environment A (width of street: 8 m).

when using an IR camera, it is necessary that the IR rays beemitted in certain patterns; hence, it is challenging to determinea technique that captures images of cyclical patterns, which areused in camera calibration. Therefore, this paper proposes a

Fig. 6. Static evaluation test environment B (width of street: 12 m).TABLE II

QUALITY NUMBER INDICATING POSITIONING ACCURACY

projection method in which a jig with thermal point sourcesarranged inside it emits IR rays, as shown in Fig. 4. In this man-ner, it is possible to determine a method of projection by usingan omnidirectional IR camera to survey the arranged multiplethermal point sources whose positions are known beforehand.

IV. COMPARISON OF STATIC POSITIONING ACCURACY

A. Outline of Static EvaluationTo confirm the effectiveness of the proposed technique, a

static positioning test was conducted. The tests were performedon (a) November 21, 2005, from 121 237 to 127 080 GPSseconds, and (b) August 22, 2006, from 195 341 to


Fig. 7. Two-dimensional positioning accuracy comparison. (a) 121 237127 080. (b) 195 341210 371.

Fig. 8. Number of satellites during static evaluation. (a) 121 237127 080. (b) 195 341210 371.

210 371 GPS seconds, at a predetermined position in the pre-mises of Kamakura Works, Mitsubishi Electric Corporation(see Figs. 5 and 6). The data were obtained at a rate of 1 Hz. Thesurroundings of the observation point comprised scattered tallbuildings and connecting corridors, which makes it a suscepti-ble environment for satellite masking. The receiver used was aZXtreme from Ashtech (currently Thales Navigation) with theelevation angle mask set at 10. The reference station was at anelectronic reference point of the Geographical Survey Instituteof Japan (Fujisawa station; baseline length of 5 km). Thepositioning computation was performed with the postprocess-ing software GrafNav 7.5, and the results were compared onthe basis of whether the computation was processed with orwithout invisible satellite data. The quality number of WaypointGrafNav, in brief, is a factor generated by GrafNav for eachoutput solution, which ranges from 1 to 6, to indicate thereliability of the solution (see Table II).

Hereinafter, the term fixed solution implies an outputsolution with a quality number of 1, float solution impliesan output solution with a quality number of between 2 and 4,and DGPS solution implies an output solution with a qualitynumber of 5 or 6. GrafNav also generates a unique indica-tor (i.e., DD_DOP) to indicate the dispersion of the satellitegeometry. When this value is extremely large, GrafNav doesnot output a result on that particular epoch. In other words, ifthe output from GrafNav is significantly deviated from the truevalue, the deviation is most likely caused by the effects of radiowave disturbances including multipath rather than the effect ofsatellite geometry.

B. Result of Static EvaluationFig. 7 shows the comparison result of the positioning accu-

racy with the exclusion of the invisible satellites, which weredetermined by using an omnidirectional IR camera. Fig. 8shows the comparison result of the number of satellites em-ployed by the receiver for positioning; the figures also showsthat there are a lot of epochs with signals received from invisiblesatellites during the static test, i.e., the receiver frequentlyreceived signals from invisible satellites. Fig. 9 shows the resultof the comparison between the position dilution of precision(DOP) before and after the exclusion of invisible satellites.From this figure, we understand that the number of satellitesused for positioning is decreased by the exclusion of theinvisible satellites, and consequently, the DOP is increased.Meanwhile, some epochs downgrade its quality because of thedecrease in received number of satellites. That will be improvedby a global navigation satellite systems upgrade like GALILEOin the near future.

Table III shows the comparison result of the ratio of fixedsolutions, float solutions, and differential global positioningsystem (DGPS) solutions at static evaluation (b). From thisresult, we find that the number of epochs with outputs that arefixed solutions is increased by positioning with visible satelliteshaving fewer multipaths by using the developed omnidirec-tional IR camera. Table IV shows the result of the comparisonof the 2-D twice the distance root mean square (2DRMS) errorsof the fixed solutions, float solutions, and DGPS solutionsbefore and after the exclusion of invisible satellites at staticevaluation (b). From Fig. 8 and Table IV, we can confirm that


Fig. 9. DOP comparison. (a) 121 237127 080. (b) 195 341210 371.TABLE III

NUMBER OF SIGNALS OF DIFFERENT POSITIONING QUALITY ATSTATIC EVALUATION (B)

TABLE IVTWO-DIMENSIONAL POSITIONING ACCURACY COMPARISON AT

STATIC EVALUATION (B)

despite the increase in the DOP due to the exclusion of theinvisible satellites, the positioning accuracy of the solutions,particularly that of the DGPS solutions, is drastically improved.Further, Fig. 9 shows the relation between each type of solutionand the GPS time, and Fig. 10 shows the relation between theGPS time and the 2-D positioning errors.

V. EXCLUSION EFFECT ASSESSMENT BYPSEUDORANGE ERROR

In this section, the results of the static evaluation (b) de-scribed in Section IVfrom 197 000 to 197 800 GPS seconds(time period A) and from 205 500 to 206 000 GPS seconds(time period B), both of which are distinctare described.Note that time period A, as shown in Fig. 9, was the timeperiod in which the DGPS solutions were improved to fixedsolutions by the exclusion of the invisible satellites. From thisfact, the invisible satellites were presumed to have considerablemultipath errors. On the other hand, in Fig. 9, the fixed solutionsare converted to float solutions or DGPS solutions by theexclusion of invisible satellites in time period B. This led tothe presumption that despite receiving signals from invisiblesatellites, fixed solutions were being computed, and diffractionwaves with small multipath errors were being received from theinvisible satellites in time period B.

To examine the above presumption, the computation of themultipath errors of invisible satellites in each time periodwas attempted. Note that the computation in this section isperformed with code multipath errors since the positioningaccuracy is significantly different before and after the exclusion

of the invisible satellites; however, the impact of multipath ongeneral carriers is minor.

A. Algorithm for Multipath ErrorsHere, an algorithm to calculate the multipath error (k)u of a

satellite k is provided. The code pseudorange for satellite k canbe expressed as

(k)u = r(k)u + I + T + c(tu ts) + (k)u (1)

where (k)u denotes the pseudorange of the user station,r(k)u denotes the satellite-to-receiver distance, I denotes the

ionospheric delay, T denotes the tropospheric delay, c denotesthe velocity of light, tu denotes the clock bias of the receiver,and ts denotes the clock bias of the satellite. In this paper, wepresume that, in comparison with the multipath errors, the noiseerrors are small enough to be ignored.

Now, when a differential correction at the reference stationwith a short baseline is applied to (1), (2), which is shownbelow, is obtained. Since the reference station used in this testwas an electronic reference point in the vicinity (with a baselinelength of approximately 5 km), we assume that

(k)ur = r(k)ur + ctur +

(k)ur . (2)

Further, taking the double difference with a differentiallycorrected reference satellite l, (2) is expressed as

(kl)ur = r(kl)ur +

(kl)ur (3)

where (k)ur denotes (k)r (k)u , r(k)ur denotes r(k)r r(k)u , turdenotes t(k)r t(k)u , (k)ur denotes (k)r (k)u , and (k)u denotesthe pseudorange of the user station.

Regarding (3), the static positioning in this test was per-formed at a known location, and the value r(kl)ur can accuratelybe calculated; thus, it is possible to calculate (kl)ur . However, themultipath error (kl)ur includes not only the multipath errors ofthe satellite but also the multipath errors of the reference stationand reference satellite l. Now, the reference station is in an openlocation where multipath hardly occurs; therefore, we presumethe error to be minor. In addition, with regard to the referencestation, we can presume the multipath errors to be minor by se-lecting a satellite with a high elevation angle that is not blockedby the surrounding buildings. With the above considerations,we compute the multipath error (k)u of satellite k as

(k)u =(kl)ur r(kl)ur

. (4)


Fig. 10. Quality number comparison. (a) 121 237127 080. (b) 195 341210 371.

Fig. 11. Carrier-to-noise ratio and multipath error. (a) PRN21. (b) PRN3. (c) PRN25.

B. Pseudorange Errors of Invisible SatellitesFirst, the multipath error of the invisible satellites in time

period A is computed. During this time period, the receiverreceives an intermittent radio wave from an invisible satellitePRN21. Fig. 11(a) shows the trajectory of PRN21 in timeperiod A and the diagram of the signal strength and the mul-tipath errors obtained by (4). Further, as a reference, the multi-

path errors of PRN3, which in the same time period were visibleat approximately the same angle of elevation as that of PRN21,are shown in Fig. 11(b).

From Fig. 11(a) and (b), it is observed that the invisible satel-lite PRN21 has extremely large multipath errors (approximately2060 m) compared to the visible satellite PRN3. In otherwords, in time period A, excluding satellites with multipath


errors as large as those of PRN21 is believed to have contributedtoward obtaining fixed solutions.

Now, a multipath mitigation technique that uses the cor-relation of the signal strength and the short-delay multipatherrors is introduced [20]. In Fig. 11(b), PRN3 with short-delaymultipath errors shows a correlation between the decrease inthe signal strength and the increase in the multipath errors;however, in Fig. 11(a), PRN21 with long-delay multipath errorsdoes not exhibit any notable correlation between the signalstrength and the multipath errors, thereby making the detectionof malfunctions from the signal strength difficult. Therefore,the positioning computation was possible with the exclusion ofPRN21 by using the proposed technique.

Second, the multipath error of invisible satellites in timeperiod B is computed. During time period B, the receiverreceives signals from invisible satellites PRN23 and PRN25;however, here, we focus on PRN25 for examination. Fig. 11(c)shows the trajectory of PRN25 and a diagram of the signalstrength and multipath errors. In this figure, it can be observedthat the multipath errors of PRN25, which should have beenan invisible satellite, are small; thus, this satellite should notbe excluded. It is believed that the signals from PRN25 werenot ground-reflected waves but diffraction waves, and thus,the multipath errors of PRN25, which should have been aninvisible satellite, became small. The following three reasonsexplain why we can presume that the signals from PRN25 werediffraction waves.

1) In Fig. 11(c), PRN25 is near the edge of a buildingand is in a condition that makes it particularly prone todiffraction waves.

2) Diffraction waves with a signal strength greater than acertain value do not have large errors.

3) In Fig. 11(c), the signal strength of PRN25 is rapidlyattenuated within a short time; such attenuation is char-acteristic of a diffraction wave.

Based on the preceding examinations, it can be stated that thesignals from invisible satellites do not necessarily include largeerrors; however, as can be confirmed from Tables II and III, it ispossible to remarkably and statistically improve the positioningaccuracy by using the proposed technique. Thus, this techniqueis effective.

VI. COMPARISON OF KINEMATICPOSITIONING ACCURACY

A. Outline of Kinematic EvaluationTo confirm the effectiveness of the technique, a kinematic

positioning test was performed on the premises of KamakuraWorks, Mitsubishi Electric Corporation. The travel route ofthe movable body is shown in Fig. 12, and the observationvehicle used in the test is shown in Fig. 13. The evaluationwas performed with an omnidirectional IR camera installedtogether with an angular displacement sensor on the roof of theobservation vehicle. The test was conducted on September 15,2006, by using a BD950 receiver manufactured by Trimble; thedata were obtained at a rate of 10 Hz. The elevation angle mask

Fig. 12. Dynamic evaluation test environment.

Fig. 13. Vehicle installed with omnidirectional IR camera and GPS.

Fig. 14. Transition of the number of satellite.

was set at 10. The observation vehicle traveled for 1 km atspeeds of less than 20 km/h.

B. Result of Kinematic EvaluationFirst, GrafNav is used to analyze the observed GPS data

without excluding the invisible satellites. The number of satel-lites used for positioning is shown in Fig. 14, and the horizontalDOP (HDOP) values are shown in Fig. 15. From Fig. 14, we


Fig. 15. Transition of HDOP.

Fig. 16. Vehicle trajectory result without proposal.

know that there are many instances where the number of satel-lites is below 4 because of the tall buildings and the connectingcorridors scattered around the observation point. Fig. 16 showsthe 2-D projection of the moving bodys position output byGrafNav. When Fig. 16 is compared with Fig. 12, we canconfirm that the moving bodys position is estimated with arelatively high accuracy, except in the region marked C (timerange from 461 530.4 to 461 544.7 GPS seconds), where theposition is obviously astray of the actual route. Meanwhile,the HDOP in region C lies in the range of 2.79 to 6.62,which is not large; the cause of this is surmised to be thefact that the positioning is performed using satellites with largemultipath errors. Further, by plotting the satellite geometry onthe omnidirectional IR camera image shown in Fig. 15, we findthat, in region C, the signals from PRN3, PRN7, PRN11,PRN16, and PRN19 were received; however, PRN7 was aninvisible satellite. In fact, there were four visible satellites andone invisible satellite. Therefore, it may be presumed that PRN7had large multipath errors in region C. Given these factors, wepostprocessed region C once again on GrafNav after automat-ically excluding the invisible satellites by using the proposedtechnique, and the results are shown in Fig. 17. Here, in theregion where the position was greatly misaligned, we can seea significant improvement through the positioning computation

Fig. 17. Vehicle trajectory result with proposed multipath mitigation process.

with the exclusion of PRN7, with which the multipath errors arepresumed to be large. From these results, it can be stated thatthe proposed technique is effective in kinematic positioning aswell as static positioning.

VII. CONCLUSION

Against the background of the increasing number of satellitesin the future, it is essential to establish a technology that canselect satellites with small multipath errors to realize highlyaccurate positioning at all times. Thus, in this paper, we haveproposed a technique with which the obstruction of satellitesignals can be determined using an omnidirectional IR camera,allowing improvement of the accuracy of mobile positioning inurban areas by excluding the invisible satellites.

With the proposed system, static and kinematic evaluationsin which the invisible satellites were discriminated through ob-servations using an omnidirectional IR camera were conducted.Hence, signals were received even while the satellites werehidden behind buildings, and the exclusion of satellites havinglarge errors in the positioning computation became possible.The test results confirmed the effectiveness of the proposedtechnique and the feasibility of highly accurate positioning.

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[31] J.-I. Meguro, T. Murata, H. Nishimura, Y. Amano, T. Hasizume, andJ.-I. Takiguchi, Development of positioning technique using omni-directional IR camera and aerial survey data, in Proc. IEEE/ASME Int.Conf. Adv. Intell. Mechatron., Sep. 47, 2007, pp. 16.

Jun-ichi Meguro received the B.Eng., M.Eng., andD.Eng. degrees from Waseda University, Tokyo,Japan, in 2003, 2005, and 2008, respectively.

Since 2006, he has been a Japan Society for thePromotion of Science (JSPS) Research Fellow withthe Advanced Research Institute for Science and En-gineering, Waseda University. He has been workingon problems related to unmanned vehicles and theirnavigation and control systems. His research inter-ests are mainly in the area of measurement controlsystems applied to intelligent vehicles.

Taishi Murata received the B.Eng. and M.Eng. de-grees in 2006 and 2008, respectively from WasedaUniversity, Tokyo, Japan, where he is currently agraduate student.

His interests are in Global Positioning Systemtechnology, mobile mapping systems, and uniquepositioning systems.

Jun-ichi Takiguchi received the B.Eng., M.Eng.,and D.Eng. degrees from Waseda University, Tokyo,Japan, in 1984, 1986, and 2004, respectively, andthe M.Se. degree from the University of Edinburgh,Edinburgh, U.K., in 1998.

Since 1987, he has been with Kamakura Works,Mitsubishi Electric Corporation, Kamakura, Japan,where he works in the field of guidance control.Since 2006, he has also been a Visiting AssociateProfessor with Waseda University.

Yoshiharu Amano received the B.Eng., M.Eng., andDr. Eng. degrees from Waseda University, Tokyo,Japan, in 1991, 1994, and 1998, respectively, all incontrol engineering.

He was a Research Associate, Visiting Lecturer,and Assistant Professor and has been a Professorwith Waseda University since 2008. He is interestedin the analysis and optimization of power and energysystems.

Takumi Hashizume received the B.Eng., M.Eng.,and D.Eng. degrees from Waseda University, Tokyo,Japan, in 1974, 1976, and 1980, respectively.

He was a Research Associate, Lecturer, and Assis-tant Professor and has been a Professor with WasedaUniversity since 1987. He is interested in power andenergy systems.

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gps multipath mitigation for urban area using omnidirectional infrared camera

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