IEICE TRANS. COMMUN., VOL.E98–B, NO.11 NOVEMBER 2015

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PAPER

Estimating Living-Body Location Using Bistatic MIMO Radar in
Multi-Path Environment
Keita KONNO† , Student Member, Naoki HONMA†a) , Member, Dai SASAKAWA† , Student Member,
Kentaro NISHIMORI†† , Senior Member, Nobuyasu TAKEMURA††† , Tsutomu MITSUI†††† ,and Yoshitaka TSUNEKAWA† , Members

SUMMARY This paper proposes a method that uses bistatic MultipleInput Multiple-Output (MIMO) radar to locate living-bodies. In thismethod, directions of living-bodies are estimated by the MUltiple SIgnal
Classification (MUSIC) method at the transmitter and receiver, where theFourier transformed virtual Single-Input Multiple-Output (SIMO) channel
matrix is used. Body location is taken as the intersection of the two directions. The proposal uses a single frequency and so has a great advantage
over conventional methods that need a wide frequency band. Also, thismethod can be used in multipath-rich environments such as indoors. An
experiment is performed in an indoor environment, and the MIMO channels yielded by various subject numbers and positions are measured. The
result indicates that the proposed method can estimate multiple living-bodylocations with high accuracy, even in multipath environments.
key words: MIMO, living-body location, DOA estimation, microwave sensor, array antenna

1. Introduction
In recent years, the increase in the elderly living alone has
become a key issue in several countries. Because of this, accidental falls and lonely death have become social problems,and calls for a safety confirmation system for the elderly
are growing. The use of video cameras and electrocardiograms (ECG) are mentioned as the conventional approaches
to safety confirmation [1]. However, the former has problems with privacy violation, the heavy burdens placed on themonitor, and its restriction to line-of-sight (LOS) situations.
The latter is handicapped by the physical and mental burdens caused by continual and direct contact of the probe on
the human body. Therefore, a safety confirmation systemthat can monitor the health and the position of the livingbody without these problems is needed.
An attractive solution is to use microwave sensors forsafety confirmation [2]–[9]. This approach does not violate
privacy, and body contact is not needed. A vital sign observation method that uses Multiple-Input Multiple-Output(MIMO) technology was proposed by Nango et al. [8]. This
Manuscript received January 28, 2015.
Manuscript revised July 11, 2015.
†
The authors are with Graduate School of Engineering, Iwate
University, Morioka-shi, 020-8551 Japan.
††
The author is with Graduate School of Science and Technology, Niigata University, Niigata-shi, 950-2181 Japan.
†††
The author is with Nippon Institute of Technology, Saitamaken, 345-8501 Japan.
††††
The author is with the Samsung R&D Institute Japan CO.
LTD, Yokohama-shi, 230-0027 Japan.
a) E-mail: honma@m.ieice.org
DOI: 10.1587/transcom.E98.B.2314

method can detect vital signs even if the antenna and theliving-body are widely separated (about 6.0 m). However,the location of the living-body cannot be estimated.
MIMO radar in the microwave band has been mentioned as a localization method [10]. However, localization
methods for vital sign sources using MIMO radar have notbeen studied adequately. Furthermore, estimation accuracy
is degraded in multi-path environments due to multi-pathreflections when we use the radar in indoor environments.
To solve this problem, Adib et al. proposed to estimate
the location of living-bodies by using Frequency-Modulated
Continuous-Wave (FMCW) radar containing multiple transmitting and receiving antennas [9]. In this method, thedistance between antennas and targets is measured by estimating the round trip time-of-flight (TOF), and target locations are estimated by applying the relationship between
this distance and multiple antenna locations. However, thismethod requires wide bandwidth, 1.79 GHz (from 5.46 GHz
to 7.25 GHz) to obtain adequate distance resolution. Moreover, this method has the problem that propagation information in static environments is essential to eliminate unwanted
components such as waves reflected from walls, non-living
objects, and so on. Because of this, it is expected that estimation accuracy will be significantly degraded if the positions of furniture in the monitoring area are changed.
The authors have proposed a method of estimating
living-body direction that uses a single frequency in multipath environments [11]. In actual multi-path environments,
estimating living-body direction has been diﬃcult becauseof the existence of unwanted components as describedabove. Our proposal uses the Fourier transformed channel matrix, which is observed by a Single-Input MultipleOutput (SIMO) radar, and the channel components alteredby the living-body are extracted. Living-body direction isestimated by applying the MUltiple SIgnal Classification(MUSIC) method [12] to the extracted channel components.
Using a single frequency for estimating living-body direction is superior to conventional localization methods thatneed wide frequency bandwidth. Though this method canestimate living-body direction even in an indoor environment, living-body location could not be determined since
the distance between the antenna and living-body could not
be estimated.
The authors also proposed a method of living-body localization using bistatic MIMO radar as an extension of thework [13], [14]. The MIMO configuration can estimatec 2015 The Institute of Electronics, Information and Communication Engineers
Copyright KONNO et al.: ESTIMATING LIVING-BODY LOCATION USING BISTATIC MIMO RADAR IN MULTI-PATH ENVIRONMENT

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both Direction of Departure (DOD) and Direction of Arrival (DOA) at the same time. In this method, living-bodydirections are estimated at the transmitter and receiver, andliving-body location is estimated as the intersection of these
directions. When multiple targets exist, however, multiple
living-body directions are estimated at the same time but
incorrectly because false images are generated. Also, thetransmitter and receiver directly face each other. With thisantenna arrangement, no intersection can be found for a target standing directly between the transmitting and receiving antennas. The bistatic MIMO radar suitable for multiple living-bodies has been introduced by authors [15], andsome fundamental results have been shown. Nevertheless,the estimation performance was not suﬃciently evaluated in[13]–[15].
In this paper, a method that uses bistatic MIMO radar
to localize multiple living-bodies is presented and evaluated.
In this method, living-body directions at transmitting andreceiving antennas are estimated by applying the MUSIC
method to the fluctuating components extracted from virtual SIMO channels, and localization is performed by finding the intersection points of these two directions. Multiple target locations can be estimated through the use of theMUSIC method utilizing both DOD and DOA as functions.
This method uses a single frequency, and static channels are
not necessary. Moreover, the antennas are angled 45 degrees into the monitoring area to virtually eliminate deadzones. Experiments are carried out in an indoor environment to show the eﬀectiveness of the proposed method. The
results demonstrate that this method can estimate multiple
living-body locations even in multi-path environments.
2. Method of Estimating Living-Body Location
Figure 1 shows the concept of the proposed method. This
study uses bistatic MIMO radar with an N element linear array transmitter and an M element linear array receiver. θT x
and θRx are the living-body directions of target x at the trans-mitter and the receiver, respectively. Target x is located at
the intersection of θT x and θRx . θT x and θRx are estimated by
MUSIC method which is a DOA estimation method. However, in multi-path environments, localization is diﬃcult because of unwanted paths such as the direct path, the waves
scattered from walls, non-living objects, and so on.
To realize localization, our method first excludes theunwanted components and observes only the fluctuating
components created by human body motion. The principleof the method is reviewed below [15].
In an indoor environment where there are L targets, themeasured M×N time-variant MIMO channel is expressedas,⎞⎛⎜⎜⎜ h11 (t) . . . h1N (t) ⎟⎟⎟
⎟⎟⎟
⎜⎜⎜
..
..
..
⎟⎟⎟ ,
H(t) = ⎜⎜⎜
(1)
.
.
.
⎠⎟
⎝⎜
h M1 (t) . . . h MN (t)where, hmn is the complex channel response from transmitter n to receiver m, and t represents the time of channel observation. M×N bistatic MIMO radar can be considered as
MN×1 virtual SIMO radar [10]. Here, the M×N MIMO
channel matrix, which encompasses the target, is expressedas the MN×1 virtual SIMO channel. This is expressed as,h(t) = [h11 (t), · · · , h M1 (t), · · · , h MN (t)]T ,(2)

where, {·}T means transposition. While DOD and DOA canbe estimated using this virtual SIMO channel, estimating
living-body location is diﬃcult because of the existence of
unwanted path components. To solve this problem, thisstudy calculates the frequency response matrix by Fouriertransformation of the time-variant virtual SIMO channel as
expressed by,
F( f ) = [F11 ( f ), · · · , F M1 ( f ), · · · , F MN ( f )]T .

(3)

Using the frequency response matrix, the correlation matrix,
RF , is expressed as,
RF = F( f )H F( f )( f1 ≤ f ≤ f2 ),(4)

where, {·}H means complex conjugate transposition and {·}represents the averaging operator. f1 and f2 delineate thefrequency range that encompasses the vital sign eﬀects. By
eigenvalue decomposition, RF is expressed as,
RF = U F ΛF U H
F,(5)

where, U F and ΛF represent the eigenvector and the diagonal matrix representing eigenvalues, respectively. They are
expressed as,
U F = [uF1 , ..., uFL , ..., uF MN ],ΛF = diag([λF1 , ..., λFL , ..., λFMN ]).

(6)
(7)

At this time, the eigenvalues, ΛF , are related as follows,
λF1 ≥ · · · ≥ λFL > λFL+1 = · · · = λF MN = σ2f ,Fig. 1

Concept of living-body localization method.

(8)

where, σ2f represents the expected value of the energy of

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the channel fluctuation component caused by the influence of noise. The eigenvector corresponding to noise,[uFL+1 , . . . , uFMN ], is expressed as U N . U N are the vectors
directing nulls to the signal from the target. Also, the steering vector corresponding to the virtual SIMO channel iscalled the virtual MIMO steering vector and is expressedas,a(θT , θR ) = at (θT ) ⊗ ar (θR ),− j 2πλ dt sinθTat (θT ) = [1, e− j 2πλ dr sinθRar (θR ) = [1, e(9)− j 2πλ dt (N−1)sinθT T

] ,

(10)

− j 2πλ dr (M−1)sinθR T(11),··· ,e,··· ,e] ,Table 1 Measurement conditions.
Antenna element (Tx/ Rx)
4 horizontal patch antenna
Element spacing (Tx/ Rx)
0.5λ
Height of antenna, h
1.0 m
Distance between Tx and Rx, D
4.0 m
Frequency
2.47125 GHz
Tx power
7.0 dBm
Channel measurement time
50 s
Snapshot rate
7.0 HzExtracting frequency range
f1 = 0.02 Hz, f2 = 3.3 Hz

where, at (θT ) and ar (θR ) are the steering vectors at the transmitting and receiving side and represent the plane wave fromθT and θR directions, respectively. ⊗ represents the Kronecker product, λ represents wavelength, and dt and dr are
the element spacing of transmitter and receiver, respectively.
By utilizing the eigenvector corresponding to noise, U N , andthe virtual MIMO steering vector, a(θT , θR ), the evaluation
function of the MUSIC method (MUSIC spectrum) is calculated as,aH (θT , θR )a(θT , θR ).
(12)
P MUS IC (θT , θR ) =aH (θT , θR )U N U H
N a(θT , θR )This MUSIC spectrum reaches peak value when θT and θR
correspond to living-body directions at transmitting and receiving antennas. Therefore, living-body directions at transmitting and receiving antennas, θT 1 ∼ θT L and θR1 ∼ θRL canbe estimated by finding peak values of the MUSIC spectrum. Living-body locations can be estimated by finding theintersection of θT x and θRx .
3. Measurement Conditions
Table 1 and Fig. 2 show measurement conditions and measurement system, respectively. In these measurements, a
4×4 bistatic MIMO configuration was used. As shownin Fig. 2(a), Single-Pole 4 Throw (SP4T) switch was usedat transmitting side. A continuous wave (CW) signal at
2.47125 GHz was used, and transmitted power at the antennas was set to 7.0 dBm. The CW signal is shared with thereceiver side since accurate synchronization between transmitting and receiving sides is needed. At the receiver side,
received signals are input to a down-converter unit by way
of Low-Noise Amplifier (LNA) unit. The down-converted
baseband signals (I1 , Q1 , ∼ I4 , Q4 ) are digitized by dataacquisition unit (DAQ) with a sampling frequency 12.5 kHz.
However, the snapshot rate for MIMO channel is determinedby the switching speed of SP4T. The transmitter and the receiver arrays consist of four horizontally arranged patch antennas with half wavelength element spacing. A photo of thearray is shown in Fig. 2(b). The thickness, width, and heightof the transmitting and receiving antennas are 1.6, 255, and
62 mm, respectively. The patch antennas are formed on a
PTFE substrate. The array’s center was set to h = 1.0 m, thechest height of the subjects. The distance between transmitting and receiving antennas, D, was set to 4.0 m. The directions of the transmitting and receiving array are definedFig. 2

Measurement system.

as, αT and αR , respectively. As an example, the directions oftarget 1 from the transmitting and receiving array are shown,
too.
Several situations with various numbers of subjects,
locations of subjects, were tested. In each trial, the timevariant channels were measured for 50 second periods. The
frequency range most strongly influenced by human heart
rate runs from 0.6 to 3.3 Hz, while that most strongly influenced by human respiration runs from 0.33 to 0.5 Hz [16].
Also, body sway aﬀects the lower frequency range. Because of this, in this study, f1 and f2 were set to 0.02 Hz and3.3 Hz, respectively. In this approach, living-body location
estimation is realized by eliminating the unwanted components and observing only the fluctuating components createdby human body motion, heartbeat, and respiration. Notethat the rate for taking a snapshot of the MIMO channel
is 7.0 Hz, which is mainly determined by switching speed

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Fig. 4

Fig. 3Time variation of SISO channel.

Measurement environment.

of the transmitting antennas. Even though the exact observation time is not same for all elements in MIMO channel
matrix, the time diﬀerences in the elements are negligibly
short compared to the speed of change in channel caused byliving-bodies.
Figure 3 shows the measurement environment. The experiment was carried out in a room containing desks andshelves. The room has concrete walls, and its width, depth,
and height are 7.0, 6.0, and 2.7 m, respectively. On one sideof the room, there are four windows. The time-variant channels were measured while targets were stationary at (XA1 ,
YA1 )∼(XAL , YAL ). Here, XAx and YAx are the actual locations
at which targets were standing. Subscript x represents target number. Also, target locations are labeled from No. 1 toNo. 72. When the receiving antenna is located at the origin
and the transmitting antenna is located at (D, 0),the location,(XAx , YAx ), is transformed to the angular values by,
YAx
XAx
YAx
−1
θT x = αT − tan,
D − XAxθRx = −αR + tan−1

Fig. 5Frequency response of time-variant SISO channel.

(13)
(14)

where the condition, YAx ≥ 0, needs to be assumed.
As an evaluation criterion of the accuracy, the acceptable error distance is introduced in this study. Since theshoulder width of the subjects is about 0.5 m, 90-th percentile error under 0.5 m is considered as acceptable.
4. Results
Figure 4 shows the time variation of the Single-Input SingleOutput (SISO) channel measured with and without a target.
When there was a single target, its standing position was
No. 37. This SISO channel is the components of the firstcolumn of the virtual SIMO channel. This figure indicatesthat the MIMO channel with a living-body has large time
variation compared to that without a living-body. It is foundthat the MIMO channel is greatly influenced by human body
motion.

Fig. 6

An example of localization result when one target was present.

Figure 5 shows the frequency response of the timevariant SISO channel measured with and without a target.
When there was a single target, the standing position of asingle target was No. 37. This figure indicates that the vital sign eﬀects appear at low-frequencies. Accordingly, thefluctuating components from 0.02 to 3.3 Hz were extracted
to observe the variation component influenced only by thevital signs. Moreover, it is found that the spectrum withliving-body widely spreads. The major reason is body sway
and respiration. They cause not only their vibration frequencies but also high-order spurious frequencies.
Figure 6 shows an example of the localization resultof the proposed method for a single target standing at position No. 11; i.e. (XA1 , YA1 ) coordinates of (3.5 m, 1.5 m). As
shown in this figure, the peak value of the MUSIC spectrum
appears near the actual location of the target. In this case,

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Fig. 7 Localization results from 72 measurement positions when a single
target was present.

the estimated location (X1 , Y1 ) coordinates were (3.56 m,
1.42 m), so location estimation error was 0.1 m. This result
confirms that the living-body localization algorithm works
even in multi-path environments.
Figure 7 shows the localization results for a single target at each of the 72 measurement positions. In this figure,the diamonds and circles indicate estimated and actual locations of the target, respectively. This figure confirms that theestimated location lies close to the actual location of the target. Even though the maximum estimation error was 0.55 m,
the most of the estimated locations are within acceptable error, i.e. 0.5 m. Therefore, living-body locations can be estimated with high accuracy regardless of target location if thenumber of target is one.
Figure 8 shows the photo and results of the experiments, where localization accuracy was evaluated for 100
trials with two targets. The photo presents the location of
the targets as well as the measurement system, which was
explained in the previous section. The standing positionsof target 1 and target 2 were No. 21 and No. 52, respectively.
Their actual (XAx , YAx ) coordinates were (3.0 m, 2.0 m) and(1.0 m, 2.5 m), respectively. As shown, the estimation error
is small.
Figure 9 shows localization accuracy results from 100
trials when three targets were present. The (XAx , YAx ) coordinates of targets 1, 2 and 3 were (4.0 m, 2.0 m), (2.0 m,
2.0 m), and (0 m, 2.0 m), respectively. Clearly estimation
accuracy is degraded compared to the two target case, but
the results roughly indicate three target locations. It can beseen that the location error of target 1 is degraded seriously
and this is because there is a concrete pillar just next to thetarget 1 as shown in Fig. 3. It is supposed that this concrete
pillar disturbs the reflected signal from target 1. Also, there
are several spurious images that stray from the target locations. The main reason is supposed that the reflected signals
from multiple targets are strongly correlated incidentally.
This can be happened because this MIMO radar observes
the Doppler components mainly caused by the respirations,where those behaviors of multiple subjects sometimes be-

Fig. 8 Localization accuracy results from 100 trials when two targets
were present.

Fig. 9 Localization accuracy results from 100 trials when three targets
were present.

come similar.
Figure 10 shows the Cumulative-Distribution-Function(CDF) of distance error for various numbers of targets. The
CDF values shown were obtained by mixing the estimatederror of multiple targets. We find that the 90-th percentile error was 0.14 m, 0.32 m, and 0.96 m for one-, two-, and threetarget cases, respectively. The error corresponding to threetarget case exceeds the acceptable accuracy, and the discussion about the accuracy degradation for this case was mentioned in the previous paragraph. It is found multiple living-KONNO et al.: ESTIMATING LIVING-BODY LOCATION USING BISTATIC MIMO RADAR IN MULTI-PATH ENVIRONMENT

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Fig. 10

Fig. 11

CDF of distance error with various numbers of targets.

Distance error with two targets separated by various distances.

body locations up to two targets can be estimated with acceptable accuracy, but the estimation error for three targets
exceeds the acceptable accuracy. Nevertheless, it is considered the error can be reduced by increasing the observation
period or number of the antennas. Though the 90-th percentile location error in [9] is about 0.5 m for the multiple
targets up to five, our results are comparable if the numberof the target is up to two. Also, it must be noted that we used
CW whereas the work [9] uses 1.79 GHz bandwidth.
Figure 11 shows the distance error with two targets separated by various distances. In this measurement, the location of target 1 is fixed at No. 28, and the location of target 2
is varied from No. 37 to No. 44, No. 53, No. 60 and No. 69.
The results shown are the average values of the distance errors over 5 estimation trials. As shown, the distance error
exceeds 1.0 m when the distance between the two targets
is 0.5 m. This occurs because the two peaks in the MUSIC spectrum overlap. However, the estimation accuracy
improves a greater separation distances. The distance error
held to under 0.5 m when the separation exceeds 1.5 m.
Figure 12 shows an example of the localization resultswith two targets at various locations. As shown, the estimation accuracy falls when the two target directions are overlap
as seen from the transmitting antenna. On the other hand,both target locations are well estimated when the two target
directions do not overlap from either antenna.
Figure 13 shows localization accuracy results with two

Fig. 12
cations.

An example of localization results with two target at various lo-

Fig. 13
cations.

Localization accuracy evaluation with two targets at various lo-targets at various locations. In this measurement, the MIMO
channel was measured while changing the standing positionof target 2 in the periphery of the measurement area; target
1 remained stationary at position No. 29. Also, the numberof trials for each situation was set to four. In this figure,the circles indicate the cases whose the average value of the
distance errors over 4 estimation trials were less than 1.0 m;
the x-marks indicates cases with large estimation error. It isfound that the proposed method is accurate for most combinations of two target locations.

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Fig. 14
ods.

CDF of distance error with various channel measurement peri-ronments. Living-body direction at the transmitter and thereceiver are estimated by applying MUSIC to the Dopplershifted channel created by movement of the living-body, andlocalization is performed by finding the intersection point ofthese two directions.
Experiments confirmed that the maximum estimation
error of this method was 0.55 m for a single target standingin 72 measurement positions. Also, the estimation error atthe 90% value of CDF for two and three targets was 0.32
m and 0.96 m, respectively. Furthermore, it was confirmedthat the estimation error was held to less than 0.5 m when
two targets are separated by more than 1.5 m. Moreover,it was found that the estimation accuracy was improved byincreasing the channel measurement period and increasing
the number of antenna elements. These results confirm thatthe proposed method can accurately estimate the locationsof living-bodies even in multi-path environments.
Acknowledgements
This research was partially supported by the Telecommunications Advancement Foundation.
References

Fig. 15

CDF of distance error with various numbers of antenna elements.

Figure 14 shows the CDF of distance error for various channel measurement periods. The two targets stoodat positions No. 21 and No. 52. The channel measurement
period was set to 1, 10, and 50 seconds. This figure confirms that the distance error at the 90% value of CDF was
2.1 m, 0.39 m, and 0.32 m for measurement periods of 1, 10,
and 50 seconds, respectively. This is because the correlation among the reflected signals from multiple-targets cannot be calculated correctly if the observation period is not
suﬃcient. That is, estimation accuracy is improved by increasing the channel measurement period.
Figure 15 shows the CDF of distance error with various
numbers of antenna elements (NoE). The single target stoodat position No. 37. As shown in this figure, the distance error
at the 90% value of CDF was 0.14 m, 0.25 m, and 1.1 m at
NoE values of four, three and two, respectively. Clearly theestimation accuracy is degraded significantly when the number of antenna elements is two. This is because the numberof eigenvectors used in the MUSIC spectrum is decreased
by decreasing the number of antenna elements. Therefore,
estimation accuracy is improved by increasing the numberof antenna elements.
5. Conclusion
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Keita Konno
received the B.E. in electricaland electronic engineering from Iwate University, Morioka, Japan in 2013. He is currently
with NTT east corporation.

Naoki Honma
received the B.E., M.E., andPh.D. degrees in electrical engineering from Tohoku University, Sendai, Japan in 1996, 1998,
and 2005, respectively. In 1998, he joined the
NTT Radio Communication Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He is now working for
Iwate University. He received the Young Engineers Award from the IEICE of Japan in 2003,
the APMC Best Paper Award in 2003, the Best
Paper Award of IEICE Communication Society
in 2006, and the APMC2014 Prize in 2014, respectively. His current research interest is MIMO antenna system and its applications. He is a member of IEEE.

Dai Sasakawa
received the B.E. in electrical and electronic engineering from Iwate University, Morioka, Japan in 2014. He is currently in the master program in Iwate University.
His research interest is array antenna calibration
for living-body radar. He received IEEE Sendai
Section Best Paper Prize in 2014.

Kentaro Nishimori
received the B.E., M.E.
and Ph.D. degrees in electrical and computer engineering from Nagoya Institute of Technology,
Nagoya, Japan in 1994, 1996 and 2003, respectively. In 1996, he joined the NTT Wireless Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in japan. He was senior research engineer on NTT Network Innovation Laboratories. He is now associate professor in Niigata University. He was a visiting
researcher at the Center for Teleinfrastructure(CTIF), Aalborg University, Aalborg, Denmark from Feb. 2006 to Jan.
2007. He was an Associate Editor for the Transactions on Communications for the IEICE Communications Society from May 2007 to May 2010
and Assistant Secretary of Technical Committee on Antennas and Propagation of IEICE from June 2008 to May 2010. He received the Young Engineer Award from IEEE AP-S Japan Chapter in 2001, Best Paper Awardof Software Radio Society in 2007 and Distinguished Service Award fromthe IEICE Communications Society in 2005, 2008 and 2010. His main interests are spatial signal processing including MIMO systems and interference management techniques in heterogeneous networks. He is a memberof IEEE. He received IEICE Best Paper Award in 2010.

Nobuyasu Takemura
received B.E., M.E.
and Ph.D. degrees in electrical engineering from
Tokyo University of Science, Tokyo, Japan,
in 1996, 1998 and 2010, respectively. From1998 to 2007, he joined the Information Technology Research and Development Center of
Mitsubishi Electric Corporation, Japan. From2007 to 2014, he joined the ET Center of Samsung R&D Institute Japan. He is currently associate professor in Nippon Institute of Technology. His research interests are antennas formobile communication systems, MIMO systems, satellite communication
systems, and radar systems. Dr. Takemura received the Young Investigators
Award from the Institute of Electronics, Information and Communication
Engineers (IEICE) of Japan in 2003. He is a member of the IEEE.

Tsutomu Mitsui
received the B.E. degrees
from Musashi Institute of Technology, Tokyo,
Japan, in 1987. In 1987, he joined Tokyo Keiki
Inc., Tokyo, Japan. Since 1999, he has been with
the Samsung R&D Institute Japan. His current
research interest is array antenna systems.

Yoshitaka Tsunekawa
received the B.E.
degree from Iwate University, Morioka, Japan,
in 1980 and the M.E. and the Doctor of Engineering degrees from Tohoku University, Sendai, Japan, in 1983 and 1993, respectively. Since
1983, he has been with the Faculty of Engineering, Iwate University, where he is now a professor at the Department of Electrical Engineeringand Computer Science. His current research interests include digital signal processing and digital control. He is a member of the Instituteof Electronics, Information and Communication Engineers of Japan, andIEEE.