IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

1

Fast Living-Body Localization Algorithm for
MIMO Radar in Multi-path Environment
Dai Sasakawa, Student Member, IEEE
Naoki Honma, Member, IEEE
Takeshi Nakayama, Shoichi Iizuka Non Member, IEEE

Abstract—A fast living-body localization algorithm using timedifferential channel suitable for multi-path environments is
introduced. In this method, a time-differential channel is calculated from the difference among the observed channels that
correspond to known biological activities such as respiration andheartbeat. The living-body locations are estimated by applying
two-dimensional MUltiple SIgnal Classification (MUSIC) to thecorrelation matrix calculated from the time-differential channels.
Experiments were carried out in an indoor environment, andthe results show that the proposed localization algorithm could
well estimate multiple device-free living-bodies simultaneously.
The experimental results demonstrate that the locations of up to
three persons could be estimated faster, within a few seconds,
and more accurately than the conventional method. One, two or
three targets yield 90% values of localization errors of 0.23 m,
0.41 m and 1.29 m, respectively.
Index Terms—MIMO radar, DOA/DOD estimation, livingbody localization, microwave sensors.

I. I NTRODUCTIONECENTLY, the populations of many countries are aging
rapidly. The increase in the number of elderly people
is increasing the need for safety-monitoring systems that can
detect people in distress due to events such as falls or sudden
cardiopulmonary arrest. Conventional safety-monitoring systems include video cameras [1] or electrocardiography (ECG)[2]. However, the former can violate privacy, place heavy
burdens on the monitoring system, and is provided only lineof-sight coverage. The latter forces the elderly to wear an
instrument that can measure heartbeat, and places excessive
physical and mental burdens on the user due to the direct andcontinuous wearing of the probe.
To avoid these problems, living-body localization methods
that use microwave radar are being studied. The use of
microwaves yields several key advances; privacy protection,ease of installation and use, and non-line-of-sight coverage.
Accordingly, microwave radar can monitor all daily life
areas and detect safety-related events such as falls. Two
approaches have been investigated for human localization:
Direction of Arrival (DOA) /Direction of Departure (DOD) based Multiple-Input Multiple-Output (MIMO) radar systems[3][4] and TDOA (Time Difference of Arrival) estimation[5][6]. Unfortunately, the former cannot localize targets in
multi-path environments because the wave reflected from thetarget suffers interference by the strong multi-path waves common in actual environments. Though the latter approach canquickly localize living-bodies by using Frequency-Modulated
Continuous-Wave (FMCW) radar in a multi-path environment,Rthis method requires wide bandwidth, 1.79 GHz (from 5.46
GHz to 7.25 GHz).
Object localization based on the MUltiple SIgnal Classification (MUSIC) method has been reported by Miwa et al.
[7]. This system uses a low-frequency band, 250 MHz, andestimates the target location by using spherical-mode MUSIC
to process the oscillating return signal. However, this method
requires observation periods of over 10 seconds and the array
aperture is comparable to the estimation distance because of
the low frequency.
The authors have, for multi-path environments, proposed aliving-body localization method that uses MIMO radar [8]-[10]based on DOA estimation using the Fast Fourier Transform(FFT) [11]. In this method, the fluctuating components createdby the living-bodies is extracted from the Fourier-transformed
virtual Single-Input Multiple-Output (SIMO) channel [3]. The
target directions from the transmitting and receiving antennas
are estimated by applying the MUSIC method to the fluctuating components. The target locations are taken to be theintersections of DOA (direction of arrival) and DOD (directionof departure). However, this method needs to observe thechannel for several tens of seconds to accurately capture
human activity information.
The authors have also proposed fast living-body direction
estimation where the time-differential channel is used toattain rapid DOA estimation in multi-path environments [12].
This method uses SIMO radar and excludes undesired waves
by taking the differences among the observed channels that
correspond to cyclic human activities such as respiration andheartbeat. Though living-body direction can be estimated byapplying the DOA estimation method to the time-differential
channel, this antenna arrangement could not localize a target
because only a single transmitting element was used. The proposal also could not estimate the directions of multiple targets
because the rank of the correlation matrix is degenerated by
the instantaneous subtraction of the observed channel.
This paper presents a fast living-body localization algorithm that uses the time-differential channel in multi-path
environments. The proposed algorithm identifies the differences among observation times that correspond to cyclic
human activities such as respiration and heartbeat. We define
this calculated channel as the instantaneous time-differential
channel. The living-body locations are estimated by applying
two-dimensional MUSIC method [15] to the time-differential
channel. This algorithm has an observation period that corresponds to one cycle of biological activity. Furthermore, its key

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

2

the observed Mt × Mr MIMO channel is converted into the
Mr Mt × 1 virtual SIMO channel,
Amplitudeh(t)Observing time

Fig. 1. Concept of biological signal.

feature is that this method does not require measurement of
the static environment in advance, which is expected to yield
immediate detection of the abnormal state of the target, as it
utilizes biological activity including respiration and heartbeat.
Moreover, the advantage of the proposed method is that thehighly accurate localization is realized using the narrow bandsignal even with the multiple passive targets, i.e. device-free
human-bodies. While our prior paper confirmed that timedifferential channels are effective for localization by estimating
the target location and trajectories [16], it did not address theestimation of multiple targets and no quantitative evaluationof estimation error was made. Therefore, this paper shows thetheoretical foundation of the proposed method. Experiments
carried out in an actual indoor environment demonstrate thatthe median value of localization error is better than 0.5 m.
II. FAST L IVING - BODY L OCALIZATION A LGORITHM
USING T IME - DIFFERENTIAL C HANNEL
The authors previously proposed a living-body localization
method that Fourier transforms the output of MIMO radar in
multi-path environments [8]-[10]. However, this method has aproblem in that channel measurements over several tens ofseconds are needed to localize targets. In this section, theauthors propose a fast living-body localization algorithm thattreats time-differential channels in multi-path environments.
The following explains the algorithm in detail.
This study assumes bistatic MIMO radar with an Mr
element linear array receiver and an Mt element linear arraytransmitter. In a multi-path environment containing L persons,the Mr × Mt time-variant MIMO channel that is captured isexpressed as,
h11 (t) . . . h1Mt (t)


..
..
..
H(t) = 
(1)
,
.
.
.
hMr 1 (t) . . .

hMr Mt (t)where, hmn is the complex channel response from the nth transmitter element to the m-th receiver element, and t
represents the observation time. Mt ×Mr bistatic MIMO radar
can be considered as Mr Mt × 1 virtual SIMO radar[3]. Here,=[h11 (t), · · · , hMr 1 (t), · · · , hMr Mt (t)]T ,(2)

where, {·}T means transposition. Then, the observed channel includes desired components from targets and undesired
components such as direct waves from transmitter to receiver,reflected waves from walls, furniture, and so on. In order toestimate living-body locations by DOA and DOD from thevirtual SIMO channel, it is necessary to exclude the undesired
waves.
The proposed method calculates the time-differential channel to exclude undesired waves. Fig. 1 shows the conceptual
diagram of the received biological signal. The signal is fluctuated by biological activities such as respiration, heartbeat, andbody motion. Here, the instantaneous time-differential channel
is defined by the virtual SIMO channel at t subtracted fromthe channel after tsb , and expressed by,hsb (t, tsb )= h(t) − h(t + tsb ).

(3)

The undesired components present in the observed channel
are stationary in time. Therefore, the instantaneous timedifferential channel excludes the undesired components, andthe variation components created by the living-bodies are
extracted.
First, we need to define the time difference tsb that will accurately extract the variation components. We start by defining
the maximum and minimum periods of the cyclic biologicalactivities as Tmax and Tmin , respectively. In most cases,
respiration is slower than or body motion so the maximum
period Tmax corresponds to respiration, and heartbeat or body
motion corresponds to minimum period Tmin . As shown in
Fig. 1, the time difference tsb is one half the cycle of biologicalactivity when the gap between the received biological signal
h(t) and h(t + tsb ) is biggest. Therefore, the range of thetime difference tsb is defined as Tmin /2 ≤ tsb ≤ Tmax /2.
Also, the time-variant channel is observed for long enough toinclude one cycle of biological activity. Therefore, observing
time t is defined as 0 ≤ t ≤ Tmax /2.
Here, the instantaneous correlation matrix using the timedifferential channel hsb (t, tsb ) with observing time t and timedifference tsb is defined as,
R(t, tsb ) = hsb (t, tsb )hsb (t, tsb )H ,(4)

where, {·}H means complex conjugate transposition. The rankof the instantaneous matrix R(t, tsb ) is degenerate because
it is extracted from an instantaneous time-differential channel. Therefore, we cannot estimate multiple target locations
from the instantaneous correlation matrix. In order to recovereigenvalue rank, an averaged correlation matrix is calculated
by averaging on both axes, the observing time and the timedifference. This is expressed as,
Rave= R(t, tsb )0 ≤ t ≤ Tmax /2, Tmin /2 ≤ tsb ≤ Tmax /2 ,(5)

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

Rx arrayTx array3

Patch antenna

PTFE substrate

D = 4.0 m

h = 1.0 m
SP4THuman Y [m]
body

LNA
DC
Rx

X [m]

FixtureTx

Fig. 2. Measurement setup.
Fig. 3. Photo of array antenna.

where, {·} is the averaging operator. In an actual measurement, the time-variant channel has a discrete variable, so theaveraged correlation matrix is expressed as,
M

Rave

=

M

=

N

=

N

Tmin
1 XX
R(mTs , nTs +),M N m=0n=0
2
Tmax
+ 1,
2Ts
(Tmax − Tmin )
+ 1,
2Ts

(6)
(7)
(8)

where, Ts means the snapshot acquisition period. By eigenvalue decomposition, the averaged correlation matrix Rave is
given by,Rave=

U ΛU H ,

(9)

U

=

[u1 , ..., uL , ..., uMr Mt ],

(10)

Λ

=

diag([λ1 , ..., λL , ..., λMr Mt ]),(11)

where, U and Λ represent the eigenvector and the diagonal
matrix representing eigenvalues, respectively. At this time,
eigenvalues Λ are related as follows,
λ1 ≥ · · · ≥ λL > λL+1 = · · · = λMr Mt = σf2 ,(12)where, σf2 represents the expected value of the energy of
the channel fluctuation component caused by the influence ofnoise. The noise eigenvalue, [λL+1 , ..., λMr Mt ], corresponding
to eigenvector, [uL+1 , ..., uMr Mt ], is expressed as U N . Here,
we use the two-dimensional MUSIC algorithm with sphericalmode vector; the original MUSIC method [13] [14] extended
to cover the 2D domain [15]. The MUSIC spectrum peaks
are found by applying the 2D-MUSIC method with its virtual
MIMO steering vector to the averaged correlation matrix,
and the spectrum peaks represent estimated target locations.
The proposed method has the theoretical upper limit of thenumber of the target, which is determined by the number ofthe receiving and transmitting antenna elements. In this case,the limit is (Mr × Mt − 1), which corresponds to the numberof the eigenvalues minus one.
III. M EASUREMENT C ONDITIONS AND E XPERIMENTAL
E NVIRONMENT
Fig. 2 shows the measurement setup. The experiments used
a 4 × 4 bistatic MIMO configuration. As shown in Fig. 2,
a Single-Pole 4 Throw (SP4T) switch was used at the transmitting side. Though the exact observation time was not thesame for all elements in the MIMO channel matrix, the time

Fig. 4. Array element patterns of the patch array antenna.

differences among the elements are so short compared to thevital activity that they are ignored. A continuous wave (CW)
signal at 2.47125 GHz was used, and transmitted power at theantennas was set to 7.0 dBm. At the receiver side, received
signals were input to a down-converter (DC) unit by wayof a Low-Noise Amplifier (LNA) unit. The down-converted
baseband signals were received by a data-acquisition unit
(DAQ). The receiver and transmitter used four horizontallyarranged patch antennas with half wavelength element spacing.
Fig. 3 shows a photo of the array antenna used in thesemeasurements. An array antenna used a PTFE substrate, andits thickness, width (including four antenna elements) andheight were 1.6, 255 and 60 mm, respectively. The array’s
center was set to h = 1.0 m, the trunk height of the subjects.
The straight line distance between transmitting and receiving
antennas was set to 4.0 m. The receiver and the transmitter
faced the center of the room.
Fig. 4 shows the measured array element patterns of thepatch array antenna that consists of four elements. The tested
area is covered within the half-power beam-width because
the antennas (receiver and transmitter) faced the center of theroom.
Measurements were made for various numbers of subjects
in different locations. The frequency range most strongly
influenced by human respiration runs from 0.6 to 3.3 Hz,
while the frequency range most strongly influenced by humanheart rate runs from 0.33 to 0.5 Hz [17]. Therefore, in thisstudy, time difference tsb was set to 0.14 [s] ≤ tsb ≤ 1.71[s]. Prior to measurements, the observation channels were
measured for 3.29 seconds. After that we evaluate the effective
observation time by the localization accuracy. The snapshot
acquisition period of the MIMO channels was set to 1/7 of asecond. The transmission components of S parameters wereIEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

Partition

4

Window
2

Y [m]
4.0

Im( ) [V]

1

3.0
2.0

-1

7.0 m

1.0
Door

0

-2

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0
0

Shelf

-2

Transmitter

1.0

2.0
3.0
X [m]

-1

4.0

0

1

2

Re( ) [V]Desk

Fig. 7. Static channel response in the complex plane.

6.0 m

Fig. 5. Experimental environment.
2

Tx

Rx

Im( ) [V]

1

0

-1

-2
-2

Targets

-1

0

1

2

Re( ) [V]Fig. 8. Channel response with one person; complex plane representation.
Fig. 6. Photo of two targets in the experimental environment.

used as the propagation channel. The number of targets, L,
was determined following the MUSIC method.
Fig. 5 shows the experimental environment. The environment was a cluttered room containing furniture and fixtures
such as tables and shelves along the walls. The room had
concrete walls and its width, depth, and height were 7.0, 6.0,
and 2.7 m, respectively. One side of the room had are four
windows. Fig. 6 shows a photo of a time-variant channel
measurement. When the channel was observed, there was only
target, and the targets stood facing the wall against which theantenna were set.
The array antenna had to be calibrated because of the havephase error created by the Radio Frequency (RF) front-end.
The target or targets stood at predetermined places and measurements were made. The calibration value was determinedby applying the SIMO radar calibration method [18] extended
for MIMO radar.
As the accuracy metric, this study uses acceptable error.
Because human body width can be approximated as 0.5 m, 90th percentile estimation error better than 0.5 m is considered
to be acceptable.
IV. M EASUREMENT R ESULTS
Fig. 7 and Fig. 8 show examples of the time-variant channel
response (complex plane representation) without/with a living-

body, respectively. In Fig. 7, the received signals show almost
no variation because the propagation environment was notchanged. In comparison with the static channel response, thetime-variant channel with a living-body of Fig. 8 exhibitschange because the living-body disturbs the transmission environment. Additionally, the variation demonstrates the periodicity of the biological activities.
Fig. 9 shows an observed time-variant channel response withand without one target. The channel response in this figure ish11 (t) of the measured Mr × Mt time-variant MIMO channel
matrix. The power of h11 with the living-body is loweredby the fading in the multi-path environment. The amplitudeof the raw channel coefficient does not represent the signal
strength of the vital sign, i.e. the channel is the sum of thepaths via the living- and non-living bodies. Again, the presenceof the living-body clearly perturbs the observed channel, andthe variation by the vital sign periodically vibrates h11 .
Fig. 10 shows an example of the time-differential channel
calculated from the measured time-variant channel in Fig. 9.
In this figure, the horizontal axis represents the observing
time difference tsb , and the time-differential channel plots
the difference between the preliminary observation time, 3.29
sec, and observing time t. This figure shows that the timedifferential channel with living-body exhibits a periodic variation corresponding to biological activity with a peak at
t = 1.29 sec. The net signal-to-noise ratio (SNR) is the ratio of

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

5ெ௎ௌூ஼ [dB]
0

4.0
-19.5

2

3.0

Y [m]

Amplitude [dB]-20

w/o living-body-20.5

Target
4

2.0
6

-21

1.0

w/ living-body

0.0

Rx

Tx

0.0

-22
0

0.5

1

1.5

2

2.5

3

8

Spectrum
peak

-21.5

1.0

3.5

2.0

3.0

10

4.0

X [m]

Observing time: t [s]Fig. 11. Example of the MUSIC spectrum for localization when one target
was present.

Fig. 9. Example of the time-variant channel.

ெ௎ௌூ஼ [dB]
0

4.0

-35
1

3.0

Y [m]

Amplitude [dB]

Target2

Target1-40-45

w/ living-body-50

w/o living-body

2

2.0

3

-55
1.0

-60
-65

0.0

-70

0

0.5

1

1.5

2

2.5

3

Rx
0.0

3.5

Tx
1.0

3.0

4.0

Fig. 12. Example of the MUSIC spectrum for localization when two targets
were present.

ெ௎ௌூ஼ [dB]0

4.0
Target2

Target1

Target3

1

3.0

Y [m]

the vital sign strength over the noise power. As for the timedifferential channel, only the variant component (vital sign)
is extracted from the raw channel. In Fig. 10, the amplitudeof the time-differential channel with the target represents
the reflected power from the target, which corresponds tothe fluctuation of the variation of the body surface by thevital activity. Meanwhile, the time-differential channel without
living-body shows no regularity. The intensity of the transitionof the time-differential channel shows the net SNR of themeasured data because the waveforms with and without aliving-body represent the vital sign and noise. This resultshows that the SNR is sufficient to observe the vital sign of theliving-body. Therefore, biological information can be extracted
from the observed channel by calculating the time-differential
channel.
Fig. 11 shows an example of the MUSIC spectrum forliving-body localization when there is one person at (X =2.0 m, Y = 2.0 m). In this figure, the circle and diamond
represent the actual target location and the spectrum peak (i.e.
estimated location), respectively. This figure confirms that theestimated location lies close to the actual location of the target.

2.0

5

X [m]

Observing time difference: T [s][s]Fig. 10. Example of the time-differential channel.

4SpectrumTargets
peaks

2

2.0

3
4

1.0

0.0

SpectrumTargets
peaks

Rx
0.0

1.0

2.0

Tx
3.0

5

4.0

X [m]
Fig. 13. Example of the MUSIC spectrum for localization when three targets
were present.

Therefore, the proposed method can well estimate living-bodylocation in multi-path environments.
Fig. 12 shows an example of the MUSIC spectrum forIEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

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80

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60
One target w/ proposal
One target w. FFT method
Two targets w/ proposal
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One target w/ proposal
One target w. FFT method
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CDF [%]

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CDF [%]

100

CDF [%]

100

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(a) 4 snapshots CDF (1200 trials)(b) 8 snapshots CDF (1200 trials)

One target w/ proposal
One target w. FFT method
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Three targets w/ FFT method

0

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1

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One target w/ proposal
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Three targets w/ FFT method

40
20
0

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0

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Estimation error [m](d) 32 snapshots CDF (1000 trials)

1(c) 16 snapshots CDF (1200 trials)
100

0

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Estimation error [m]

100

20

0

Estimation error [m]

100

40

One target w/ proposal
One target w. FFT method
Two targets w/ proposal
Two targets w/ FFT method
Three targets w/ proposal
Three targets w/ FFT method

40

1

1.5

2

Estimation error [m](e) 64 snapshots CDF (500 trials)(f) 128 snapshots CDF (200 trials)

Fig. 14. CDF of estimation error for each snapshot.

1.5

1.2

Estimation error [m]Estimation error [m]1.4

90% error w/ proposal
90% error w/ FFT method

10.8Median error w/ FFT method
Median error w/ proposal

0.6
0.4

Human width

0.2
0

0

20

40

60

80

100

120

140

Number of snapshots

90% error w/ proposal
90% error w/ FFT method1

Human width
Median error w/ FFT method
Median error w/ proposal

0.5

0

0

20

40

60

80

100

120

140

Number of snapshots

Fig. 15. Median and 90% error versus the number of snapshots with one
target.

Fig. 16. Median and 90% error versus the number of snapshots with twotargets.

localization with two people. Target1 and target2 stood at(X = 1.0 m, Y = 2.5 m) and (X = 3.0 m, Y = 2.0 m),
respectively. In this figure, the spectrum peaks appear withineach target circle (centered on actual location). This result
confirms that the proposed method can estimate the locationsof multiple targets by recovering eigenvalue rank.
Fig. 13 shows an example of the localization result for threepeople. Target1, target2, and target3 stood at (X = 0.0 m,
Y = 2.0 m), (X = 2.0 m, Y = 2.0 m), and (X = 4.0 m,
Y = 2.0 m), respectively. In this figure, the spectrum peaks oftarget2 and target3 lie within the target circle, while the peak oftarget1 is close to target1’s circle. Thus, the estimation error oftarget1 is about 0.5 m. This confirms that the proposed method
can localize even three targets simultaneously. We discuss theaccuracy of estimation later using a statistical characterization.
Next, we evaluate the estimation error by the observation
time (i.e. the number of snapshots). In the proposed algorithm,

when the total observation time tob was less than Tmax =3.3 [s], the observing time t and the time difference tsb were
defined as 0 [s] ≤ t ≤ tob − Tmax /2 [s].
Fig. 14 shows the cumulative distribution function (CDF)of the localization error estimated by the proposed method
and a conventional method (FFT technique [10]) with one,
two and three targets, respectively. The localization error was
defined by the Euclidean distance between the actual target
locations and the estimated positions. The estimation error
means the averaged error of each target localization error when
there were two and three subjects. All plots in Fig. 14 showthat the proposal yields better estimation accuracy than theconventional method regardless of the number of targets andsnapshots. However, it is confirmed that estimation accuracy
is low with just a few snapshots. Fig. 14(a) shows the CDF of
the estimation error with 4 snapshots (i.e. the observation timeis 0.43 seconds). The 90% values of the proposed method areIEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

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3

2

1.5

Estimation error [m]Estimation error [m]90% error w/ target1
90% error w/ target1
90% error w/ target2

1

Human width
Median error w/ target1
Median error w/ target2

0.5

090% error w/ target2

2
90% error w/ target3

1.5

Human width
Median error w/ target1
Median error w/ target2
Median error w/ target3

1
0.5
0

0

20

40

60

80

100

120

140

Fig. 17. Median and 90% error values of each target versus the number ofsnapshots with two targets: proposed method.

1.8
90% error w/ proposal
90% error w/ FFT method

1.6
1.4
1.2
1

Human width

Median error w/ FFT method
Median error w/ proposal

20

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0.8
0.6
0.4
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0

20

40

60

80

100

120

140

Number of snapshots

Number of snapshots

Estimation error [m]

2.5

120

140

Number of snapshots
Fig. 18. Median and 90% error versus the number of snapshots with threetargets.

0.86 m, 1.15 m and 1.52 m for one, two and three targets,
respectively. These values exceed our definition of acceptableerror. Fig. 14(b) shows the CDF of the estimation error with
8 snapshots; observation time is 1.00 second. In this figure,the 90% values are 0.52 m, 1.03 m, and 1.38 m for one, twoand three targets, respectively. While these values also exceed
the acceptable error, they are clearly better than the 4 snapshot
case. Fig. 14(c) shows the CDF of the estimation error with 16
snapshots; observation time is 2.14 seconds. In Fig. 14(c), the
90% values of the proposed method are 0.32 m, 0.96 m and1.30 m for one, two and three targets, respectively. here, the
90% value with one target does satisfy the acceptable error.
We mention the factor of the estimation error with multiple
targets later. Fig. 14(d) shows the CDF of the estimation error
with 32 snapshots,; observation time is 4.43 seconds. The 90%
values of the proposed method are 0.23 m, 0.41 m and 1.29 m
for one, two and three targets, respectively. The 90% value ofthe proposed method with one or two subjects is lower than
the acceptable error. On the other hand, since the 90% valueof the conventional method is 0.82 m, i.e., over the acceptableerror, the FFT method requires longer observation time. In
Fig. 14(e) and (f), the observation times are 9.00 and 18.14
seconds, respectively. Observing the channel for longer periods
improves the accuracy. Note that the proposal and conventional

Fig. 19. Median and 90% error values of each target versus the number ofsnapshots with three target: proposed method.

method yield similar results in Fig. 14(f). In particular, theestimation accuracies with three subjects were still inadequate.
Fig. 15 shows the median and 90% value of the localization
error versus the number of snapshots with one target. In
this figure, though the 90% values of the proposal and theconventional method satisfy the acceptable error at the sametime, the former has error of 0.32 m which is superior to thatof the latter, 0.43 m. It can be seen that the proposed method
yields a 0.11 m improvement in localization accuracy with
16 snapshots. Also, all median values of the estimation error
results are below 0.2 m.
Fig. 16 shows the median and 90% value of the averaged
localization error versus the number of snapshots with two
subjects. The median averaged error with the proposed andFFT method were within the acceptable error. Of particularinterest, the proposal attained median errors below 0.3 m
for each snapshot number examined. The largest accuracy
difference between the proposal and conventional method(90% value) is 0.41 m with 32 snapshots (= 4.43 [s]). Withtwo subjects, the 90% value of the FFT method exceeds theacceptable error, while the 90% value of the proposal is better
than 0.5 m.
Fig. 17 shows the median and 90% errors of the proposed
method for each target. The channel was measured withtarget1 and target2 standing at (X, Y ) = (1.0, 2.5) [m] and(X, Y ) = (3.0, 2.0) [m], respectively. In this figure, thelocalization results of target2 are more accurate than thoseof target1 for all observation time, which is reasonable giventhat target2 was closer to the transmitter.
Fig. 18 shows, for the proposed and conventional method,
the median and 90% averaged localization error versus snapshot number with three targets. Though the median and 90%
error values improve as snapshot number is increased to 16,
the median values exceed the acceptable error. The estimation
accuracy basically saturates beyond 16 snapshots, and so canbe considered as the limit of three subject localization.
Fig. 19 shows the median and 90% error of the proposed
method for each of three targets in isolation. Target1, target2,
and target3 stood at (X, Y ) = (0.0, 2.0) [m], (X, Y ) =(2.0, 2.0) [m] and (X, Y ) = (4.0, 2.0) [m], respectively.
In this figure, the median localization error of each target

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. XX, NO. Y, MONTH 20XX

V. C ONCLUSION
This paper proposed a fast living-body localization algorithm that treats time-differential channels in multi-path
environments. A time-differential channel is calculated as thedifference among the observed channels generated by biological activities; the differencing eliminates constant unwanted
components such as direct waves, reflected waves from walland so on. The proposed algorithm can also localize multiple
targets by recovering eigenvalue rank by averaging in terms ofobservation duration and time difference. We estimate target
locations by applying the 2-dimensional MUSIC method tothe averaged correlation matrix by casting the system as a
virtual SIMO array. Experiments were carried out in an indoor
environment. The results showed that with one subject, 2.14

12
10

Estimated

8
Y [m]satisfies the acceptable error at 16 snapshots. The difference
between those results and the median error in Fig. 18 is thesimultaneous presence of the targets. The proposal can localize
one or two targets accurately, but is weak when there are threesubjects simultaneously. It can be seen that paucity in the
degree of freedom available for the estimation is the cause of
the degraded localization accuracy. The degree of freedom isfour in this experiment as we used 4by4 MIMO radar. Because
the proposed and conventional method used MUSIC [13], only
one degree of freedom was used when localizing three targets.
A simple solution is to increase the number of MIMO antenna
elements. Regarding the estimation results of target3, though
the accuracy was short of the acceptable error, localization was
far more precise than for the other targets as it was closest to
the transmitter.
Fig. 20 shows the tracking result when the target wasapproaching the transmitter. The 8 × 8 MIMO configuration
was used for this demonstration. The receiver and transmitter
have eight horizontally arranged patch antennas that are justextended array of the configuration shown in the Fig. 3. The
antenna thickness, width, and height were 1.6, 480 and 60 mm,
respectively. The element spacing was half wavelength. The
distance between the transmitter and the receiver was set to
8.0 m. A CW signal at 2.47 GHz was used, too. In this trial, thetime window length for detecting the target was 2.56 seconds.
The period of taking snapshots of the MIMO channels per
second was set to 50 Hz. During the measurement, the target
walked straight at the speed of 0.7 m/s from (0, 8) [m] to(7, 1) [m]. In this figure, the solid line indicates the actual
movement line, and the circles represent the locations of thetarget estimated by the proposed algorithm. In this figure, 94
points were estimated. At the start position (X, Y ) = (0, 8)[m], there were circle like movement in the estimated locations
because the subject did not start walking at this moment. Also,
there were three outliers from the actual movement line in the
results, and the maximum error was 6.36 m. Meanwhile, mostof the estimated error from the actual movement line is within
the acceptable error (91 points /94 points=96.8%). The medianvalue of the estimated error from the movement line was 0.18m, and the averaged error was 0.35 m. This result shows thatthe proposed method can successfully estimate the trajectoryof the walking target in the conference room.

8

Actually moved

6
4
2

Direction of movement
Transmitter

Receiver

0
0

2

4

6

8

X [m]

Fig. 20. Tracking result of the walking target; the target moved straightly
from (0, 8) [m] to (7, 1) [m] at the speed of 0.7 m/s.

s measurement periods yielded localization error below 0.5
m. In the case of two targets, 4.43 s measurement periods
yielded a 90% value of the averaged estimation error under
the acceptable error limit. For three targets, though the 90%
value exceeded the acceptable error, the median error of eachtarget was less than 0.5 m. Moreover, in case of multiple
subjects, the location of the target closest to the transmitter
was detected most accurately. These results confirm that the
proposed method can accurately localize living-bodies within
few seconds in multi-path environments.
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[11] Keita Konno, Masaki Nango, Naoki Honma, Kentaro Nishimori,Nobuyasu Takemura, and Tsutomu Mitsui, “Experimental evaluationof estimating living-body direction using array antenna for multipath
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Dai Sasakawa received the B.E. and M.E. degree
in electrical and electronic engineering from Iwate
University, Morioka, Japan in 2014 and 2016, respectively. He is currently in the Ph.D. program in
Iwate University. He received the Young Engineers
Award from the IEICE of Japan in 2017. His current research interest is MIMO systems for humanmonitoring.

Naoki Honma received the B.E., M.E., and Ph.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 Telegraphand Telephone Corporation (NTT), in Japan. He is
now working for Iwate University. He received theYoung 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 2014 Asia-Pacific Microwave ConferencePrize in 2014, respectively. His current research interest is MIMO system andits applications. He is a member of IEEE.

9

Takeshi Nakayama received the B.E., M.E. degrees in Department of Opt-electronic & MechanicalEngineering, Faculty of Engineering from Shizuoka
University, Shizuoka, Japan in 1994, 1996, respectively. In 1996, he joined the Multimedia Development Center, Matsushita Electric Industrial Co., Ltd.
(Panasonic Corporation), in Japan. He is now working for Panasonic Corporation. His current researchinterest is MIMO system and its applications.

Shoichi Iizuka received the B.E. and M.E. degrees
in information science and technology from Osaka
University, Osaka, Japan in 2013 and 2015, respectively. He joined Appliances Company, Panasonic
Corporation in 2015. His current research interest
is living-body radar system and its applications. He
is a member of Information Processing Society ofJapan.