IEICE TRANS. COMMUN., VOL.E100–B, NO.3 MARCH 2017465

PAPERParametric Wind Velocity Vector Estimation Method for SingleDoppler LIDAR Model

Takayuki MASUO†a), Student Member, Fang SHANG†, Shouhei KIDERA†, Tetsuo KIRIMOTO†,Hiroshi SAKAMAKI††, and Nobuhiro SUZUKI††, Members

SUMMARY Doppler lidar (LIght Detection And Ranging) can provideaccurate wind velocity vector estimates by processing the time delay andDoppler spectrum of received signals. This system is essential for real-time wind monitoring to assist aircraft taking off and landing. Consideringthe difficulty of calibration and cost, a single Doppler lidar model is moreattractive and practical than a multiple lidar model. In general, it is impos-sible to estimate two or three dimensional wind vectors from a single lidarmodel without any prior information, because lidar directly observes onlya 1-dimensional (radial direction) velocity component of wind. Althoughthe conventional VAD (Velocity Azimuth Display) and VVP (Velocity Vol-ume Processing) methods have been developed for single lidar model, bothof them are inaccurate in the presence of local air turbulence. This paperproposes an accurate wind velocity estimation method based on a paramet-ric approach using typical turbulence models such as tornado, micro-burstand gust front. The results from numerical simulation demonstrate thatthe proposed method remarkably enhances the accuracy for wind velocityestimation in the assumed modeled turbulence cases, compared with thatobtained by the VAD or other conventional method.key words: Light Detection and Ranging (lidar), single lidar model, localair turbulence estimation

1. Introduction

Low-level windshear related to local turbulence is one of themain factors which can lead to aircraft accidents in landingor taking off [1]. Since the operation of real-time monitor-ing systems commenced with terminal Doppler wind radar(TDWR) at the beginning of the 1990s in the United States[2], a number of signal processing approaches for wind ve-locity measurement techniques using Doppler radar havebeen actively investigated [3], [4]. However, Doppler radarsystems work only when it rains, because it requires an echofrom raindrops. In recent years, the remote sensing tech-nique with lidar (light detection and ranging) has enteredinto the spotlight as a technique for velocity estimation andwind shear detection [5]. lidar measures the wind velocityby analyzing the echo scattered by atmospheric aerosols andmolecules. Subsequently, not only for rainy weather, the li-dar can be generally used in all weather conditions, exceptfor heavy rain or snow fall. Basically, there are two opera-

Manuscript received July 6, 2016.Manuscript revised September 14, 2016.Manuscript publicized October 12, 2016.†The authors are with Graduate School of Informatics and En-

gineering, The University of Electro-Communications, Chofu-shi,182-8585 Japan.††The authors are with Information Technology R&D Center,

Mitsubishi Electric Corporation, Kamakura-shi, 247-8501 Japan.a) E-mail: masuo@secure.ee.uec.ac.jp

DOI: 10.1587/transcom.2016EBP3265

tion modes of the lidar system. Multiple lidar systems canmeasure the general two dimensional(2-D) wind velocity inreal atmospheric situation, however, operators suffer greatdifficulties for calibrating radial velocities in such system[6]–[8]. On the contrary, a single lidar system is consideredpreferable since it does not need complicated calibration orsynchronization.

The velocity azimuth display (VAD) method is one ofthe most simple or useful models for the single lidar system[9]. The VAD method assumes that the wind has a constantspeed and direction with the same range but different az-imuth angles. This assumption makes it possible to calculatethe 2-D velocity vector field using only a single-lidar model.However, in some cases, such as local air turbulence, withsignificant variation in local area, the VAD method yieldsinaccurate velocity vector estimations. As another approachfor solving this problem, the velocity volume processing(VVP) method is developed based on linear approximationof velocity distribution in local spatial region [10]. However,in the case of non-linear distribution, this method does notachieve sufficient accuracy, naturally, due to assuming thelinear-distribution. As a solution for this problem, we havealready proposed the extended VAD method that adaptivelychanges the correlation area in the cost function [11]. Whilesome cases show that the extended VAD method achievesbetter performance in estimating wind velocity than eitherVAD or VVP, it shows still room for improvement in lo-cal air turbulence estimation accuracy. As other literature,turbulence estimation and detection methods using a singlelidar have been discussed, where the difficulty of developingaccurate wind velocity estimation is demonstrated [12].

In an aircraft landing or taking off situation, since it isrequired for the operator to judge what kind of the local tur-bulences could occur along the aircraft path, it is rather im-portant to recognize the typical local turbulences (e.g. gustfront or tornado) with its location and scale from the ob-served Doppler velocity distributions, than to know the gen-eral 2-D velocity map. According to this background, thispaper proposes the parametric window velocity estimationmethod for specific turbulence models, such as tornado orgust for single lidar model. Although the parametric ap-proach for typical tornado model has been proposed usingRanking vortex model [13], it did not introduces the com-parison or recognition of different kinds of turbulence. Theproposed method introduces mathematical turbulence mod-els, i.e., uniform distribution, tornado, microburst, and gust

Copyright c⃝ 2017 The Institute of Electronics, Information and Communication Engineers

466IEICE TRANS. COMMUN., VOL.E100–B, NO.3 MARCH 2017

Fig. 1 System model.

front, and determines the optimum parameters by minimiz-ing the residual errors between observed and estimated ra-dial velocity discrepancy. In addition, this method has no-table feature that an occurred air turbulence is directly andautomatically determined in the process of optimization. Tosolve the multi-dimensional optimization problem, we em-ploy the particle swarm optimization (PSO) is used here toavoid the local optimal solution [14]. The results of 2-D nu-merical analyses show that the proposed method achievessignificantly higher accuracy in comparison with conven-tional methods with automatically selecting an appropriateturbulence model.

2. System Model

This paper assumes the 2-D problem in monostatic lidar ob-servation. Figure 1 shows the system model. In this fig-ure, each black circle denotes a resolution cell, whose sizeis determined by the range and azimuth resolution of lidar.The parameter vr denotes the radial velocity, φ is the anglein the azimuth direction and θ is the angle of u from they axis. u denotes a wind velocity vector with components(usin(θ), ucos(θ)) in directions x and y. Wind vectors of theobservation area are expressed by using such polar coordi-nation model as (u, θ). It also assumes that the horizontalwind is invariant in the observed event.

3. Conventional Methods

This section introduces the three conventional approaches,which are typically used in single lidar issue.

3.1 VAD Method

The VAD method estimates a wind velocity vector, underthe assumption that the velocity vector is invariant in an areaof constant distance but varying in azimuth direction. Themethodology of this method is briefly explained as follows.Here, r denotes the distance between the observation celland lidar location. When the curve of the surface of theEarth is negligible, the radial velocity is defined as

vr(r, φ) =12

r(∂u∂x+∂v

∂y

)+ v0cosφ + u0sinφ

+12

r(∂v

∂y− ∂u∂x

)cos2φ +

12

r(∂v

∂x+∂u∂y

)sin2φ, (1)

where (u, v) denotes x and y components of a wind vector uin the spatial domain and (u0, v0) is a wind vector in the cen-ter of the correlation region. Assuming a uniform distribu-tion in this correlation region, the partial differential termsof Eq. (1) can be eliminated and the radial velocity can beexpressed as

vr(v(ri), θ(ri)) = vcos (θ − φ) . (2)

The wind velocity is estimated by minimizing the squaremean of residuals as(v(ri), θ(ri)

)= argminv(ri),θ(ri)

∑ri∈Ωi

vr (vh(ri), θ(ri)) − vr,obs(ri)2,

(3)

where Ωi denotes the correlation region centered on the ob-servation cell ri and vr,obs(ri) denotes the observed radial ve-locity. Given that the VAD method assumes a uniform windin the correlation region, its estimation accuracy is signifi-cantly degraded in the case of large variance in evaluatingcells.

3.2 VVP Method

The VVP method calculates the wind velocity vector bymaking a linear approximation of the spatial variance ofthe wind field. Supposing (x0, y0) is the center location ofthe observed region, the wind vector u (x, y) of the location(x, y) is expanded by making the first-order approximationof a Taylor series expansion as,

u (x, y) ≃ u (x0, y0) +∂u

∂x(x − x0) +

∂u

∂y(y − y0) . (4)

The radial velocity can then be expressed as

vr = u ·(ixsinφ + iycosφ

)= PK, (5)

where

P =

sinφcosφ

sinφ(rsinφ − x0)cosφ(rcosφ − y0)

rsinφcosφ

T

,

K =(u0′, v0

′,∂u∂x,∂v

∂y,

(∂u∂y+∂v

∂x

))T

.

Parameter ix and iy are unit vectors in directions x andy respectively, u0

′Cv0′ are defined as u0′ = u0 − ∂u∂yy0 and

v0′ = v0 − ∂v∂x x0. In general, the number of observation data

n satisfies the condition n ≫ rank(K). K is calculated usingthe least-squares method of observed radial velocities. Pa-rameter ur composed of n radial velocities is expressed by Pas ur = PK. The normal equation for K is found from

K =(PT P

)−1 (PTur

). (6)

MASUO et al.: PARAMETRIC WIND VELOCITY VECTOR ESTIMATION METHOD FOR SINGLE DOPPLER LIDAR MODEL467

Fig. 2 Adaptive correlation length in the extended VAD method.

Parameter u0′ and v0′ obtained from Eq. (6) should include

the rotation term. Since this method makes a linear approx-imation of the spatial variance of the wind field, in a nonlin-ear case such as turbulence, its accuracy degrades. In addi-tion, because the difference operation is used in calculatingthe wind velocity, the method is, in general, much sensitiveto fluctuations of the measurement errors.

3.3 Extended VAD Method

The main problem of the VAD and VVP methods is that as-sumption of both methods which the distribution in spatialdomain is uniform or linear approximation becomes invalidunder the local air turbulence. To overcome this problem,the literature [11] have extended the original VAD methodby adaptively optimizing the spatial correlation length forthe wind field calculation, i.e., the method optimizes notonly the wind velocity vector but also the spatial correlationlength σ (ri). The three parameters are determined by(v (ri) , θ (ri) , σ (ri)

)=

argminv(ri),θ(ri),σ(ri)

∑r j∈Ωi

e− |ri−r j |2

2σ2(ri) vr (v(ri), θ(ri)) − vr,obs(ri)2∑r j∈Ωi

e− |ri−r j |2

2σ2(ri)

,

(7)

where σ (ri) denotes the correlation length in the spatial do-main. Ωi denotes the correlation region centered on the ob-servation cell ri. Figure 2 shows the adaptive correlationregion according to the wind field. This method allows us toestimate the wind velocity in the case of local air turbulenceby adaptively changing the spatial correlation length. It hasbeen demonstrated that extended VAD method can providemore accurate estimation of wind vectors than the VAD andVVP methods, but it suffered from severe inaccuracy in thecase of larger local variance cases, such as tornado or gustfront [11].

4. Proposed Method

This section presents the principle and methodology of theproposed method. As previously mentioned, all the afore-mentioned conventional methods suffer from inaccuracy for

velocity estimation, in the case of local air turbulence. As asolution to this problem, this paper introduces a parametricapproach by introducing the mathematical model for eachturbulence pattern as follows.

(1) Uniform Distribution modelv and θ are constant at all observation cells. Then,the parameter vectors in this model is defined as p1 =

(v, θ).(2) Tornado model

The tornado is approximated using the Rankine vortexmodel [17] in which the wind velocity norm reaches amaximum vc at a distance rc from the center location.This model is expressed as

∥u∥(x, y) =

vcrc

d(x, y) (d ≤ rc)vcrc

d(x, y)(d > rc)

, (8)

where d(x, y) =√

(x − xc)2 + (y − yc)2. (xc, yc) is thecoordinates of the center locations of the tornado, andthe wind direction is expressed as

θ(x, y) = tan−1(

x − xc

y − yc

)+π

2. (9)

Then, the parameter vectors in this model is defined asp2 = (xc, yc, rc, vc).

(3) Microburst modelThe microburst represents a downdraft, and generatesa strong gust in the radial direction when it hits theground. The wind velocity variation model of the mi-croburst is similar to that of tornado model using theRankine vortex model [18]. The wind direction is ex-pressed as

θ(x, y) = tan−1(

x − xc

y − yc

), (10)

where ∥u∥ is determined in Eq. (8). Then, the parametervectors in this model is defined as p3 = (xc, yc, rc, vc).

(4) Gust front modelThe gust front is the updraft generated by the collisionof air currents. The norm of the 2-D wind vector of thismodel is expressed as

∥u∥(x, y) =

vcos

(tan−1

(− σ2

D(x,y)2

))((x, y) ∈ Ω1)

−vcos(tan−1

(− σ2

D(x,y)2

))((x, y) ∈ Ω2)

,

(11)

where v denotes the constant coefficient, σ is the cur-vature coefficient of the wind velocity variation, andD(x, y) =

√(xc − x)2 + (yc − y)2 − R. Boundary of Ω1

and Ω2 is called shear line, and is drawn in a circle ofradius R. The wind direction is expressed as

θ(x, y) =

tan−1

(x − xc

y − yc

)((x, y) ∈ Ω1)

tan−1(

x − xc

y − yc

)− π ((x, y) ∈ Ω2)

. (12)

468IEICE TRANS. COMMUN., VOL.E100–B, NO.3 MARCH 2017

Fig. 3 Flowchart of the proposed method.

Then, the parameter vectors in this model is defined asp4 = (xc, yc,R, v, σ).

Using the above mathematical wind model, the turbulencemodel denoted is determined as;

k = argmink

minpk

∑r j∈Ω

(vr,est

(pk

) − vr,obs(r j))2

, (13)

where k denotes the index number of turbulence model, Ωdenotes all the observation region and pk denotes parametervector in the kth turbulence model, which includes the loca-tion or scale of each turbulence. vr,est

(pk

)is the calculated

radial velocity from kth turbulence model. This method au-tomatically selects the most appropriate model by evaluat-ing the radial velocity, and offers accurate estimation witha small number of optimization variables. In this case, toavoid the local optimization problem, the PSO algorithm isintroduced in this optimization [14]. Furthermore, this pa-per introduces the network structure into the PSO algorithmto improve a convergence performance [15], [16]. Figure 3shows the flowchart of the proposed method.

5. Performance Evaluation in Numerical Simulations

This section describes results of the numerical simulationconducted for performance analysis. In these simulations,21 and 11 cells are sampled in the range and azimuth di-rections, respectively. The observation interval is 150 m inthe range direction and 6 in the azimuth direction at therange of −30 ≤ φ ≤ 30. Note that, the characteristic ofsynoptic-, meso-, and local-scale meteorological phenom-ena is time dependent, naturally. However, it is consideredthat this time variance effect is negligible within an updatingtime of lidar, because the beam scanning velocity of lidar isestimated as 10 deg/sec, indicating that the updating rate forwind vector fields for 60 degree azimuth range is less than10 sec. Since the average motion velocity of tornado is inthe range between 10 and 20 meters, the motion amount in

Fig. 4 True wind vectors ((a): uniform distribution, (b): tornado, (c):microburst and (d): gust front).

updating interval is estimated around 60 m, which is withinthe range resolution (150 m in this case). Then, we regardthat the variance of center location or size of each turbulencecould be ignored in this case. We assess the performance ofthe proposed method for four wind field models as uniformdistribution, tornado, microburst and gust front. These tur-bulences greatly affect the safety of aircraft navigation.

True wind vectors in each wind field model are shownin Fig. 4. True parameters of each wind field model areset to p1 = (v, θ) = (10m/s, 135), p2 = (xc, yc, rc, vc) =(0 m, 1500 m, 200 m, 20 m/s), p3 = (xc, yc, rc, vc) =

(0 m, 1500 m, 200 m, 20 m/s), p4 = (xc, yc,R, v, σ) =

(−1500 m, 3000 m, 2000 m, 5 m/s, 140 m) respectively. Fig-ures 5, 6, 7 and 8 present the wind vector estimated usingthe VAD, VVP, the extended VAD and proposed methods,respectively, in noiseless situation. Here, in the case of theoriginal VAD, each cell denoted as ri is evaluated by usingtwo neighboring cells (denoted asΩi in Eq. (3)) along the az-imuth direction at the same range. Also, in the VVP method,each cell is evaluated by four neighboring cells along rangeand azimuth directions. In PSO algorithm, we set a numberof particle, replication and network-bound particles as 500,200 and 3 respectively. In particular, the estimation errorsof tornado and microburst, in the far range regions, are sig-nificantly reduced by the proposed method, while the othermethods suffer from the significant inaccuracy in such case.

For quantitative analysis of the estimation accuracyof the wind field, the normalized root mean square error(NRMSE) is introduced as

NRMSE =

√∑Ni=1 |utrue − ui|2∑N

i=1 |utrue|2, (14)

where N denotes the number of observation cells, and utrueand ui are true wind vectors and estimated wind vectors, re-spectively. Table 1 gives the NRMSE for each method. Ta-ble 1 shows that, in comparison with the VAD, VVP andthe extended VAD methods, the proposed method achieves

MASUO et al.: PARAMETRIC WIND VELOCITY VECTOR ESTIMATION METHOD FOR SINGLE DOPPLER LIDAR MODEL469

Fig. 5 Estimation results with VAD method ((a): uniform distribution,(b): tornado, (c): microburst and (d): gust front).

Fig. 6 Estimation results with VVP method ((a): uniform distribution,(b): tornado, (c): microburst and (d): gust front).

significantly lower NRMSE for the tornado, microburst, andgust front models. The estimation accuracy of gust front isslightly degraded to other models because, in this model,the number of parameters in mathematical model, namely,optimization variables, is larger than that of other models.

Next, we consider the effect of random fluctuations onthe measurement of the radial velocity, namely the evalua-tion in noisy situation. Lidar steadily includes fluctuationon radial velocity as measurement equipment error. Sincethe SNR for each observation data significantly depends ona selected filtering process or the assumed type of randomnoises (Gaussian or others), we directly add the fluctuationto observed radial velocity based on the actual lidar mea-surement, for simplicity. We add the fluctuation ∆vr for theobserved radial velocity, whose probability density functionfollows a Gaussian distribution. Standard deviation of thisGaussian fluctuation ς is varied between 0.2 m/s and 1.0 m/s.

Fig. 7 Estimation results with the extended VAD method ((a): uniformdistribution, (b): tornado, (c): microburst and (d): gust front).

Fig. 8 Estimation results with the proposed method ((a): uniform distri-bution, (b): tornado, (c): microburst and (d): gust front).

Table 1 NRMSE in each wind field model.Uniform Wind Tornado Microburst Gust Front

VAD 6.50 × 10−3 1.36 1.79 0.988VVP 1.25 × 10−7 7.45 6.15 3.41

Ex. VAD 6.50 × 10−3 0.761 1.04 0.713Proposed 3.31 × 10−4 0.166 0.162 0.191

Figure 9 shows the NRMSE versus ς. This figure show thateach NRMSE rapidly increases with increasing standard de-viation of the fluctuation ς in the conventional methods. Incontrast, the proposed method remarkably suppress the ac-curacy degradation even in more fluctuated cases.

Next, we assess effect of fluctuations on assumed math-ematical model. Since the natural wind includes the fluctu-ation of the wind velocity vector, to reproduce this case byadding the fluctuation in the parameters that constitute thewind velocity vector field, that is to say, the exact solution

470IEICE TRANS. COMMUN., VOL.E100–B, NO.3 MARCH 2017

Fig. 9 The NRMSE versus ςn ((a): uniform distribution, (b): tornado,(c): microburst and (d): gust front).

Table 2 Standard deviation of ∆pk for each parameter.

Turbulence type pk S.D of ∆pk

Uniform Wind (v, θ) (5 m/s, 10 deg.)Tornado (xc, yc, rc, vc) (150 m, 150 m, 100 m, 10 deg.)

Microburst (xc, yc, rc, vc) (150 m, 150 m, 100 m, 10 deg. )Gust Front (xc, yc,R, v, σ) (150 m, 150 m, 20 m, 5 m/s, 30 m)

Fig. 10 Estimation results in parameter fluctuations ((a): tornado, (b):microburst).

Table 3 NRMSE in each wind field model with parameter fluctuations.

Uniform Wind Tornado Microburst Gust FrontVAD 0.654 1.95 1.99 1.67VVP 2.51 6.94 6.34 4.25

Ex. VAD 0.493 1.57 1.34 1.04Proposed 0.108 0.418 0.471 0.425

for the turbulence parameters depends on the location of ob-servation cell. The parameter vectors including fluctuationsexpressed as pk = pk + ∆pk, where ∆pk denotes an errorvector from actual one. Table 2 denotes the standard devi-ation of ∆pk for each model, in this case. The left side andright side of Fig. 10 show the examples for the cases of tor-nado and microburst wind vectors by the proposed method,where parameter fluctuations in Table 2 are given. Table 3gives the NRMSE for each method in this case, and even inthe case that a unique solution does not exist.

Fig. 11 Estimation results of window velocity with mixture models ((a):Actual, (b): VAD, (c): VVP, (d): extended VAD and (e): proposed method).

Finally, to test more general case, the case where mul-tiple turbulence models are mixed together, is investigatedfor revealing the relevance of the proposed method. Here,the actual distribution of the window velocity vector fieldu(x, y) is expressed as the linear combination of each turbu-lence model as;

u(x, y) = wuniuuni(x, y; p1) + wtorutor(x, y; p2)+ wmicroumicro(x, y; p3) + wgustugust(x, y; p4). (15)

Figure 11 shows the examples of this mixtured model foreach method, where (wuni, wtor, wmicro, wgust) = (0.2, 0.5, 0.1,0.2), that is, the tornado field is relatively domi-nant. True parameters of each wind field modelare set to p1 = (v, θ) = (10m/s, 135), p2 =

(xc, yc, rc, vc) = (0 m, 1500 m, 200 m, 20 m/s), p3 =

(xc, yc, rc, vc) = (0 m, 1500 m, 200 m, 20 m/s), p4 =

(xc, yc,R, v, σ) = (−1500 m, 3000 m, 2000 m, 5 m/s, 140 m)respectively. Here, the proposed method judges thedominant turbulence is “tornado” by comparing the costfunctions in Eq. (13) among all possible turbulences.The estimated parameter of tornado model is p2 =

(8.9 m, 1453 m, 196 m,−10 m/s). The NRMSEs for eachmethod in this case are 2.75 for the VAD method, 6.84for the VVP method, 1.864 for the extended VAD methodand 1.61 for the proposed method, respectively. This re-sults show that our method has the least error in this mixture

MASUO et al.: PARAMETRIC WIND VELOCITY VECTOR ESTIMATION METHOD FOR SINGLE DOPPLER LIDAR MODEL471

model. To enhance the accuracy in this method, it is promis-ing to determine each weight value in the optimization, but italso requires more calculation time in the PSO optimization.Those problems should be treated in our future works.

6. Conclusion

This paper has proposed an accurate wind vector estimationmethod based on parametric estimation using four mathe-matical turbulence models. The results of numerical simu-lations have shown that the proposed method has has higheraccuracy in estimating the wind vector than conventionalmethods. In addition, it is found that the proposed methodmethod offers greater robustness to fluctuations on the mea-surement of the radial velocity and wind model. Note that,in the simulation of this study, the range cell resolution is setto 150 m, but in the case of smaller scale of meteorologicalphenomena, such cell resolution is not enough to determinethe parameters accurately as less data is compared to un-knowns (the degree of freedom of parameter p). However,the actual lidar measurements demonstrated higher rangeresolution of around 25 m, then, if the scale of local tur-bulence is smaller than 100 m, enough number of observa-tion cells is available by using such higher range resolutionlidar system. Since a coherent Doppler wind lidar uses apulse laser with a long pulse width, we need to considerthe trade-off between range and frequency resolutions in thiscase, which depend on the sampling frequency and samplingpoint. In addition, even in the case that the local turbulence,such as tornado, would shield the scattering echo from a tar-get behind this turbulence, it is considered that the proposedmethod would work by exploiting the observable data fromthe vicinity area. To confirm the above discussion, it is nec-essary to investigate the real observation data. It should benoted that since the literature [13] using the same Rankingvortex model successfully estimates the actual window field,it is predicted that the proposed method will also work welleven in realistic scenario.

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Takayuki Masuo received his B.E. de-grees in Electronic Engineering from Univer-sity of Electro-Communications in 2014. He iscurrently studying M.E degree at the GraduateSchool of Informatics and Engineering, Univer-sity of Electro-Communications. His current re-search interest is advanced information process-ing of LIDAR application.

Fang Shang received the B.S. and M.S.degree in electrical engineering and automationfrom Harbin Institute of Technology, China, in2009 and 2011. She received the Ph.D. degree inelectrical engineering and information systemsfrom The University of Tokyo, Japan, in 2014.She is an Assistant Professor in the GraduateSchool of Informatics and Engineering, Univer-sity of Electro-Communications, Japan. Hercurrent research interest is in the signal andimaging processing for the polarimetric SAR

and the UWB radar.

472IEICE TRANS. COMMUN., VOL.E100–B, NO.3 MARCH 2017

Shouhei Kidera received his B.E. degreein Electrical and Electronic Engineering fromKyoto University in 2003 and M.I. and Ph.D.degrees in Informatics from Kyoto Universityin 2005 and 2007, respectively. He is currentlyan Associate Professor in Graduate School ofInformatics and Engineering, the University ofElectro-Communications, Japan. His current re-search interest is in advanced radar signal pro-cessing or electromagnetic inverse scattering is-sue for ultra wideband (UWB) sensor. He was

awarded Ando Incentive Prize fo the Study of Electronics in 2012, YoungScientistfs Prize in 2013 by the Japanese Minister of Education, Culture,Sports, Science and Technology (MEXT), and Funai Achievement Awardin 2014. He is a member of the Institute of Electrical and Electronics Engi-neering (IEEE) and the Institute of Electrical Engineering of Japan (IEEJ).

Tetsuo Kirimoto received the B.S. and M.S.and Ph.D. degrees in Communication Engineer-ing from Osaka University in 1976, 1978 and1995, respectively. During 1978–2003 he stayedin Mitsubishi Electric Corp. to study radar signalprocessing. From 1982 to 1983, he stayed as avisiting scientist at the Remote Sensing Labora-tory of the University of Kansas. From 2003 to2007, he joined the University of Kitakyushu asa Professor. Since 2007, he has been with theUniversity of Electro-Communications, where

he is a Professor at the Graduate School of Informatics and Engineering.His current study interests include digital signal processing and its applica-tion to various sensor systems. Prof. Kirimoto is a senior member of IEEEand a member of SICE (The Society of Instrument and Control Engineers)of Japan.

Hiroshi Sakamaki received the B.S. andM.S. degrees in Information Engineering fromHokkaido University in 1996 and 1998, respec-tively. He joined Mitsubishi Electric Corpora-tion in 1998, where he has been engaged in re-search and development on radar signal process-ing for meteorological observations.

Nobuhiro Suzuki received the B.S. andM.S. in Electrical and Electronic Engineeringfrom Tokyo Institute of Technology in 1992and 1994 respectively. He has been workingin Mitsubishi Electric Corp. since 1994 to studysignal processing for radars, directional findersand location systems. He stayed as a graduatestudent in University of Alaska Fairbanks (UAF)from 1998 to 2001 and received Ph.D. degree ininterdisciplinary of Science and Engineering in2006. Nobuhiro Suzuki is a member of IEEE

and IEICE.