VOL. E101-B NO. 2
FEBRUARY 2018
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PAPER

Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP2016

RSSI-Based Living-Body Radar Using Single RF Front-End andTunable Parasitic Antennas
Katsumi SASAKI†a) , Student Member, Naoki HONMA† , Member, Takeshi NAKAYAMA†† ,and Shoichi IIZUKA†† , Nonmembers

SUMMARY This paper presents the Received-Signal-StrengthIndicator (RSSI) based living-body radar, which uses only a single RF
front-end and a few parasitic antennas. This radar measures the RSSI variation at the single active antenna while varying the terminations of theparasitic antennas. The propagation channel is estimated from just the temporal transition of RSSI; our proposal reconstructs the phase informationof the signal. In this paper, we aim to estimate the direction of living-body.
Experiments are carried out and it is found that most angular errors arewithin the limit of the angular width of the living-body.
key words: DOA estimation, parasitic antenna, RSSI, microwave sensors

1.

Introduction

Microwave sensors for living-body detection have been wellstudied [1]–[6]. When the microwave is irradiated on theliving-body, the amplitude and phase of the reflected waves
are changed due to the movement of the living-body surface.
To the best of our knowledge, the benefits of microwave
sensing are: 1) the unawareness living-body sensing, 2)privacy protection.
Many attempts to use array antennas for living-bodysensing have been presented [3]–[6], and they fall into twogroups. The aim of the first group [3], [4], is sensitivity enhancement of the existence detection of living-bodies. Combining the signals received at all antenna elements yields ahigh detection rate. The second group aims to find the direction or location of the living-bodies [5], [7]. For detecting thelocation of a living-body, a key advance was the MIMO radar
technique [8]. Even though the second group well works
in detecting targets, they require expensive hardware, suchas synchronized multiple transmitters/receivers, whereas thesome of the first group require just phaseless information,i.e. the Received-Signal-Strength-Indicator (RSSI).
A method of estimating a complex MIMO channel created by just a single exciting antenna and multiple parasitic
antennas with variable reactance elements has been proposed[9], [10]. These methods estimate the MIMO channel fromthe single channel response modulated by the parasitic antennas, which is called the load modulation technique. This
Manuscript received February 15, 2017.
Manuscript revised June 15, 2017.
Manuscript publicized August 22, 2017.
† The authors are with the graduate school of Engineering, Iwate
University, Morioka-shi, 020-8551 Japan.
†† The authors are with Panasonic Corporation, Kadoma-shi,
571-8501 Japan.
a) E-mail: t2316021@iwate-u.ac.jp
DOI: 10.1587/transcom.2017ISP0022

scheme yields complex MIMO channel estimation with justa single RF front-end at each side. Moreover, [10] presented
a method of restoring phase information from the RSSI ofthe channel.
By combining these methods, the authors proposed an
RSSI-based method for estimating living-body direction using parasitic antennas [11]. In this method, the RSSI of
the propagation channel is measured by a simple system
consisting of a single receiver and parasitic antennas. The
complex propagation channel is estimated from just received
RSSI. Living-body direction is estimated by extracting thevital-sign-related component from the time-varying channel. This method significantly simplifies the RF front-end
by reducing the number of receivers. Moreover, it does notneed phase information of the signal, which allows the use ofcommercially available receivers, such as Wi-Fi and Bluetooth hardware. Here, there are similar methods such as‘ESPAR antenna’ [12]. This method uses dipole antennas,
and only the DOA estimation of the signal is performed.
On the other hand, [11] uses patch antennas and focuseson the estimation of the living-body direction. Moreover,this technique enables to extract only the signal through theliving-body, excluding the fixed component such as reflected
waves from walls and floors. But in the prior study, the complex channel was measured in advance, and load modulation
was performed numerically.
This paper experimentally evaluates the living-bodyradar proposal, and so estimates target direction using only
RSSI and a single RF front-end with parasitic antennas. In
this paper, load modulation is performed by connecting variable impedances to the parasitic antennas, and measuring
the RSSI with a single RF front-end. In the rest of the paper,
the proposed method of estimating living-body direction isreviewed and detailed. After that, the results of the experiment are presented to confirm that the proposed method iseffective in estimating living-body direction.
2.

Proposed Method of Estimating Living-Body Direction

2.1

Antenna Model and Propagation Environment Model

Figure 1 shows antenna system model in the proposed
method. This system is composed of a transmitting antenna
and an active receiving antenna, and M-element parasitic
antennas terminated by load impedances. In the following,

Copyright © 2018 The Institute of Electronics, Information and Communication Engineers

SASAKI et al.: RSSI-BASED LIVING-BODY RADAR USING SINGLE RF FRONT-END AND TUNABLE PARASITIC ANTENNAS

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Fig. 1

Antenna system model.

subscripts T, R, and P, refer to the transmitting (Tx), receiving (Rx), and parasitic (Px) ports, respectively. In thisantenna system, the S-parameters are expressed as STT hT R hT P 
S =  h RT SRR h RP (1)
 h PT h P R S P P 
where, h RT represents the channel response from Tx to Rx
antennas, for example. Note that, h RT cannot be directly
observed since the signal observed at Rx also contains scattered paths from the parasitic elements and they cannot be
separated normally. h PT represents the channel response
from Tx to Px antennas, and this is not directly observable,
too. h RP are S-parameters that represent coupling between
Rx and Px antennas. Since S is a symmetric matrix, therelations, hT R = h RT , hT P = hTPT , and h PR = hTRP aresatisfied, where hT P and h RP are column vectors, and h PT
and h PR are row vector. STT , SRR , and S P P represent the Sparameters of Tx, Rx and Px antennas, respectively. Amongthe mentioned elements, hT R = h RT and hT P = hTPT correspond to the channels between the transmitter and receiversides, which is unknown and may vary due to the antennaarrangement and the environment. On the other hand, STT ,SRR , S P P , and h PR = hTRP represent the characteristicsof the antennas that are normally constant and they can bemeasured when the antennas are produced.
When the parasitic elements are terminated by load
impedance elements, whose termination condition is definedas Γ, the signal strength is given by| y | = h RT + h RP (Γ−1 − S P P ) −1 h PT |s|(2)

where s represents the signal transmitted from Tx antenna,and the termination condition isΓ = diag(γ1, . . . , γ M )(3)where γm is the reflection coefficient of the terminating loadat the m-th Px antenna. When z0 is the reference impedanceand z m is the load impedance at the m-th Px antenna, γm is
calculated asz m − z0γm =(4)
z m + z0

Fig. 2

2.2

Concept of the evaluation function.

Method of Estimating the Propagation Channel

In this study, the complex single-input multiple-output(SIMO) channel vector, [h RT , hTRP ]T , is estimated from the
RSSI response of the single RF front-end while the load
impedances at the Pxs are varied. Figure 2 depicts the concept of the evaluation function, which is one of the key ideas
in this method. At first, the temporal transition is observed
during the period corresponding to the cycle with K-set terminations. The K-sets of the load impedances at the Px arrayare defined as
 Z 1  z11
 . 
Z =  ..  =  ...
 ZK 
 z K1

...
..
.
...

z1M
..
.




z K M (5)where K represents the number of variable impedance
patterns.
Z k represents a row vector expressed as[zk1 . . . zkm . . . zk M ], where zkm represents the load
impedance at the m-th Px antenna in the case of the k-th
termination pattern. That is, in the case of the k-pattern,
the M-element Px antennas are simultaneously terminatedby the impedance set represented by Z k .
In this study, the termination conditions are changed
and the resulting signals are observed, where K patterns of
the conditions are given within a sufficiently short periodthat must be shorter than the cycle of the vital-life sign. The
cycle of K times observation described above is repeated L
times to observe the temporal variation of the channel dueto the vital sign. Here, the measurement period of L timecycle needs to be sufficiently long, and can be comparable
to the cycle of the vital-life sign. In measuring the l-th (1≤ l ≤ L) snapshot, the received signal strength in the k-th
(1 ≤ k ≤ K) termination pattern is defined as | yk(l) |.
Multi-path environments contain unnecessary waves
such as the direct wave between the Tx/Rx antennas andwaves reflected from the wall. They need to be eliminatedto estimate the living-body direction correctly. Therefore,

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the mean signal strength over a certain period is subtracted
from the observed signal strength because the mean value
corresponds to the constant channel component, which is
not thought to contain the vital sign. At the l-th measurement, the signal strength through the living-body, excluding
the fixed component, is expressed as| yk0 (l) | = | yk(l) | −

L
1X

L l=1

| yk(l) |test, P M








(7)

0
where htest,
R represents the channel between Tx and Rx
0antennas, and htest,
Pm represents the channel between Tx
and Px antennas in the m-th element. Note that the assumed
channel does not necessarily agree with the true channel atthis moment. This assumed channel is translated into an
RSSI transition using the expression of,0| ytest,k|= h 0 test, P1 
.
0
−1
..
 |s|,
htest,R+ h RP (Γ−1k −S P P )  0h
 test, P M 

(8)where the termination condition in the k-th termination pattern isΓk = diag(γk1, . . . , γk M ).

(9)

γkm represents the reflection coefficient of the terminatingload of the Px antenna in the m-th element, as expressed in(4)
The simulated RSSI transition calculated by (8) is compared with the actually observed one by using the evaluation
function defined as,Kel =1 X 0 (l)(l)
0
| y | − | ytest,k|,K k=1 k D R (θ S ) 

D P1 (θ S ) 

(l)
0

h test (θ S ) = X 
..
.


 D PM (θ S ) (6)

Now, let us explain the simulation part depicted inFig. 2. In the first step, the arbitrary complex SIMO channel
vector is assumed to be, h 0 h 0test, R
test, P1(l)0h test = ..
.
 h 0direction, the incoming wave will arrive from a certain direction. Therefore, multiple steering vectors are initially used,
where their directions are determined with uniform directional difference to cover the designated extent of detection.
We use the universal steering vector [13] defined as,(10)which is the square-mean of the value differences over K-set
trials.
After that, the initially assumed channel is iteratively
optimized so as to make the signal responses estimated fromit fit the observed responses, i.e. this is the minimization
problem detailed in (10). In this study, the steepest descent method is used to minimize el . In the steepest descentmethod, the initial value, i.e. the first assumed channel, determines the convergence and accuracy of the solution. Hence,a realistic channel vector needs to be chosen as the initialvalue. Since the aim of this study is identification of target(11)

where X is the arbitrary value determining the amplitudeof the steering vector, and D R and D P m are the complex
directivities of Rx and m-th Px antennas, respectively. The
steepest gradient algorithm is applied to the multiple steeringvectors with various directions, θ S , and we chose the bestsolution, i.e., the one with the smallest error. The target direction is calculated from the estimated channel by using the
MUltiple SIgnal Classification (MUSIC) method [14]. Here,
since the relationship between ‘the antenna-target distance’and ‘the amplitude of the received signal’ is complicated
due to the multi-path fading [15], it is difficult to estimate
the antenna-target distance accurately.
3.

Experimental Conditions and Environment

To verify the performance of the proposed method in an
actual environment, an experiment was conducted. Table 1
shows the measurement conditions. A varactor diode wasconnected to Px antennas, and its terminal condition was
controlled by the bias voltage. Antenna height H was 1.05 m,
and the distance between antennas and target was 1.5 and
3.0 m. The number of termination sets was K = 9, andchannel snapshot frequency was 100 Hz, which corresponds
to the switching time of the termination set, and the total
observation period was 9.0 s. This means the SIMO channel
vectors are estimated every 0.09 s, and the total number ofchannel estimatations in the complete observation period was
L = 100. Figure 3 shows the measurement environment, andFig. 4 shows a measurement scene. The target stood straight
upright at the position of −40◦ ≤ θ R ≤ 40◦ with respect tothe broadside direction of the array.
Figure 5 shows the measurement system. The RF signal
output from the signal generator was branched through thedivider. One signal was sent to the Tx antenna through avariable attenuator and power amplifier so that the transmisTable 1 Mesurement condition.
Antenna configuration
1×1 SISO + 2 Px elements
Antenna height H [m]1.05
Frequency [GHz]2.47
Termination sets K
9Snapshot frequency [Hz]100
Measurement time [s]9.0Gamma switching time [s]0.01
Total observation period L
100
Antenna-target distance D RT [m]1.5, 3.0
Target position θ R [◦ ]-40, -30, . . ., 40

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Fig. 5

Fig. 3

Measurement environment.
Fig. 6

Fig. 4

Measurement system.

Load modulator.

Measurement scenery.

sion power was set at 10 dBm. Figure 6 shows a photo of
the load modulator. The voltage output from the DC (direct
current) voltage generator was applied to the bias port of thePx antenna via the DA (Digital to Analog) converter. Here,an FPGA (field-programmable gate array) was used to control the voltage value from the AD converter. In order tosynchronize the control signal with the PC, a synchronization signal was sent from the DA converter to the DAQ (Data
Acquisition Unit).
Figure 7 shows the Rx and Px antennas used in thisexperiment. The receiving array consisted of three element
antennas, where the center element was the Rx antenna andother two elements were Px antennas. The inter-antennainterval (center-to-center) was about 0.4 wavelengths. In
order to enhance mechanical robustness, FR4 substrate wasused. Also, in order to achieve an array with high radiation efficiency and wide aperture, an air layer was formed
between the patch conductor layer (upper substrate) and theground layer (lower substrate). The thickness of the air layer

Fig. 7

Rx and Px antennas.

ya ir was 3.15 mm. SMA connectors were attached to thebackside of the lower substrate, i.e., each patch has a single
feed port. The Px antenna element was configured with atermination port that connects the varactor diode, and a bias
port is provided on the antenna element. Figure 7(c) shows aclose-up of the bias port. The DC cable on the backside wasconnected via a resistance, R, with value of 10 kΩ. Therefore, when DC bias is applied, a reverse bias is applied to thevaractor diode. Although the resistor and the varactor diodeare connected in series, no voltage drop occurs in the resistor
because so little DC current flows. Therefore, the DC bias is

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applied only to the varactor diode. On the other hand, since
the resistance value R is large, no RF signal flows to the bias
port. As described above, the use of the high impedance
resistance makes it possible to realize a bias port that does
not affect the RF characteristics.
4.

Results

Figure 8 shows the reflection coefficient of the varactor diodeused in this experiment. When the voltage applied to thevaractor diode, V ,is changed from 0 V to 20 V, the reflection
coefficient draws the trajectory shown in the figure. In thisexperiment, the voltage value is set so that the phase intervalof the reflection coefficients is uniform. ΓPx1 and ΓPx2 represent the reflection coefficients of the varactor diodes connected to the parasitic antennas, Px1 and Px2, respectively.
They should be same theoretically, but slightly different due
to manufacturing error. ΓPxm1 , ΓPxm2 , ΓPxm3 represent thereflection coefficients for V =0, 7.7, and 20 V, respectively.
Figure 9 shows the directivity of the Rx antenna when the
Px antennas are terminated. Here, we compared the directivity of the termination processing by numerical analysis
after directivity measurement and the directivity measuredby actually connecting the varactor diode to the Px antennas.
Since each Px antenna element is terminated by varactor
diodes of three reflection coefficients, a total of 9 directivity
patterns were observed. From the figure, it can be seen thatthe form of the directivity changes with each termination
condition.
Figure 10 shows the signal strength variation over theobservation time. Here, Fig. 10(a) shows the intensity variation over all measurements. In this figure, the variationof the respiration component is seen at a period of about
3 s. Also, Fig. 10(b) shows the close-up of (a) in the rangeof 3.9 s to 4.1 s. As indicated in this figure, the unit measurement period is defined as the period corresponding to
the cycle of the K sets of the terminations, where K = 9
in this experiment. It can be seen that the signal strengthchanges in accordance with the impedance pattern number,

Fig. 8Reflection coefficient of varactor diode.

k, and a similar waveform appears periodically at every unit
measurement period, which is set to 0.09 s.
Figure 11 shows how the steepest descent method makes
the amplitude error of the channel converge by performing
optimization so that the evaluation function of (10) becomesminimum. The horizontal axis represents the number oftrials of the steepest descent method, and the vertical represents the amplitude error defined by (10). Here, the numberof iterations of the steepest descent method was set to 50.
It can be seen the amplitude error of the channel decreased
step by step. Also, it is found that the convergence speed andFig. 9Fig. 10

Directivity.

Observed signal strength.

SASAKI et al.: RSSI-BASED LIVING-BODY RADAR USING SINGLE RF FRONT-END AND TUNABLE PARASITIC ANTENNAS

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Fig. 11

Steepest descent.

Fig. 12RSSI comparison.
Fig. 13accuracy significantly depend on the initial values, and theerror with the best initial channel converges into 2.89 times
smaller than that with the worst one does, where the best
initial value, θ S = 0◦ , just corresponds to the exact directionof the target.
Figure 12 compares the signal strength transitions of theactual observation and simulations before and after optimization. ‘Observed’ means the actually observed signal strength
through the living-body. This value is expressed by (6) inSect. 2.2. On the other hand, the dotted and dashed lines are
the values calculated by simulation. As a first step of thechannel estimation, our method assumes a complex channel
as shown in (7). The dashed line represents the response
calculated by using (8) with the initially assumed channel,
and this is indicated as ‘Simulated (un optimized).’ By substituting the optimized complex channels into that in (8), thedotted line, ‘Simulated (optimized)’, is calculated. As described in Sect. 2.2, the mean signal strength over a certain
period is subtracted from the observed signal strength. From
this result, it can be seen the optimization well predicts thesignal strength transition, that is, the complex SIMO channel
is well estimated.
In this study, the evaluation criteria for angular estimation are based on the angular width of the human body. If
the estimated angular error is less than one half the angularwidth of the human body, it is judged that the living-bodydirection was accurately estimated. For the human bodywidth of 0.50 m and the distances between the antennas andtarget of 1.5 m and 3.0 m, the acceptable error is 9.5◦ and4.8◦ , respectively.

Center value of the angular errors.

To evaluate the accuracy of the proposed method, we
conducted the DOA estimation with full RF front-ends, i.e.
all antennas individually have synchronized receivers, where
all other conditions, such as array configurations, are identical. Figure 13 shows the median value of the angularerrors versus the target direction. Here, the estimation was
performed 20 times at each target direction. In this figure,good agreement between the result with phase and without phase can be confirmed. Also focusing on the angularerror for θ R < 30◦ , estimation of direction was possible
within the acceptable angular error. Figure 14 shows the
CDF (Cumulative-Distribution-Function) of the angular error. Focusing of 50% values, when the distance between theantennas and target was 1.5 m and 3.0 m, the angular error
was 3.9◦ and 4.3◦ , respectively. Therefore, the angular error
was less than the acceptable error. From the above, it was
confirmed that the proposed estimation method is effective
when varactor diodes actually terminate the Px antennas.
5.

Conclusion

This paper has presented experiments that validate the livingbody direction estimation method that relies on just the RSSI
of a single RF front-end antenna system with several parasitic antennas. The experiments were conducted using threemicrostrip-antenna arrays terminated by varactor diodes. It
was found that the proposed method successfully identified
the direction of the target even though the environment was

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Fig. 14

CDF of the angular errors.

multipath, and the median angular error was 3.9◦ and 4.3◦
when the distance between the antennas and target was 1.5 m
and 3.0 m, respectively. This means the angular error is lessthan the angular width of the human body. From the above,
we confirmed that the proposed method well estimates the
direction of the living-body from just the amplitude information captured by a single RF front-end. These results provedthat the proposed method well estimates the living-body direction even with simple devices. In our future work, theestimation of the location and motion will be investigated byextending the proposed method.

no.4, pp 250–255, Dec. 2010.
[6] K. Konno, M. Nango, N. Honma, K. Nishimori, N. Takemura, andT. Mitsui, “Experimental evaluation of estimating living-body direction using array antenna for multipath environment,” IEEE Antennas
Wireless Propag. Lett., vol.13, pp.718–721, April 2014.
[7] K. Konno, N. Honma, D. Sasakawa, K. Nishimori, N. Takemura,and T. Mitsui, “Estimating living-body location using bistatic MIMO
radar in multi-path environment,” IEICE Trans. Commun., vol.E98B, no.11, pp.2314–2321, Nov. 2015.
[8] J. Li and P. Stoica, MIMO Radar Signal Processing, A John Wiley
Sons, 2009.
[9] N. Honma, “Method of MIMO channel estimation between parasitic antenna arrays,” IEEE Trans. Antennas Propagat., vol.61, no.5,
pp.2792–2800, May 2013.
[10] D. Pinchera and M.D. Migliore, “A phaseless parasitic MIMOchannel sounder,” Proc. IEEE Antennas and Propagation SocietyInt. Symp., 2008.
[11] K. Sasaki, N. Honma, T. Nakayama and S. Iizuka, “RSSI-based
estimation method of living-body direction using parasitic antennas,”
2016 Int. Symp. Antennas and Propagat. (ISAP), pp.924–925, Oct.
2016.
[12] E. Taillefer, A. Hirata, and T. Ohira, “Direction-of-arrival estimation
using radiation power pattern with an ESPAR antenna,” IEEE Trans.
Antennas Propag., vol.53, no.2, pp.678–684, Feb. 2005.
[13] Q. Yuan, Q. Chen, and K. Sawaya, “Accurate DOA estimation using array antenna with arbitrary geometry,” IEEE Trans. Antennas
Propagat., vol.53, no.4, pp.1352–1357, April 2005.
[14] R.O. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. Antennas Propagat., vol.AP-34, no.3, pp.276–
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Katsumi Sasaki
received the B.E. degree inelectrical and electronic engineering from Iwate
University, Morioka, Japan in 2016. He is currently in the master program in Iwate University. His current research interest is RSSI-based
living-body radar using parasitic antennas.

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[4] K. Ushiki, K. Nishimori, N. Honma, and H. Makino, “Intruder detection performance of SIMO and MIMO sensors with same numberof channel responses,” IEICE Trans. Commun., vol.E96-B, no.10,
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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 2014 Asia-Pacific Microwave Conference Prize in 2014, respectively. His current research interest is MIMO system and its applications. He is a member of IEEE.

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Takeshi Nakayama
received the B.E., M.E.
degrees in Department of Opt-electronic & Mechanical Engineering, Faculty of Engineeringfrom 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 research interest
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 ofInformation Processing Society of Japan.