IEICE Communications Express, Vol.3, No.2, 61–67

Experimental evaluation on
MIMO sensor employing
eigenvector diversity
Keita Ushiki1 , Kentaro Nishimori1a) , Naoki Honma2 ,and Hideo Makino1
1

Graduate School of Science and Technology, Niigata University,

8050, Ikarashi 2-no-cho, Nishi-ku, Niigata, 950–2181, Japan
2

Iwate University,

3–18–8 Ueda, Morioka, Iwate 020–8550 Japana) nishimori@ie.niigata-u.ac.jp

Abstract:
An intruder detection method by using time variant
Multiple Input Multiple Output (MIMO) channels has been proposed(MIMO sensor). In this letter, the detection performance using eigenvectors, which are obtained by singular value decomposition for MIMO
channel matrix, is evaluated in actual indoor propagation environment.
Moreover, a new diversity method using multiple eigenvectors is proposed to enhance the performance of the intruder detection using eigenvectors. Scenarios that are eﬀective for 1st and 2nd eigenvectors is
veriﬁed and the eﬀectiveness using multiple eigenvectors is shown compared to the MIMO sensor using the channel matrix.
Keywords: MIMO sensor, propagation channel, eigenvector, indoor
propagation environment
Classification: Antennas and Propagation
References[1] M. Nishi, S. Takahashi, and T. Yoshida, “Indoor human detection systems
using VHF-FM and UHF-TV broadcasting waves,” Proc. IEEE International Symposium on Personal Indoor and Mobile Radio Communication
2006 (PIMRC2006), Session TJ7.3, Sept. 2006.
[2] S. Ikeda, H. Tsuji, and T. Ohtsuki, “Indoor Event Detection with Eigenvector Spanning Signal Subspace for Home or Oﬃce Security,” IEICE
Trans. Commun., vol. E92-B, no. 7, pp. 2406–2412, July 2009.
[3] K. Ushiki, K. Nishimori, N. Honma, and H. Makino, “Intruder Detection
Performance of SIMO and MIMO Sensors with Same Number of Channel
Responses,” IEICE Trans. Commun., vol. E96-B, no. 10, pp. 2499–2505,
Oct. 2013.
[4] T. Sugiura, N. Honma, and K. Nishimori, “MIMO Sensor -Raytracing
Analysis of Break-in Detection Performance in Indoor Environment-,” IEICE Technical Report, A·P2010-76, Oct. 2010.
[5] Y. Karasawa, “MIMO Propagation Channel Modeling,” IEICE Trans.
Commun., vol. E88-B, no. 5, pp. 1829–1842, May 2005.
c

IEICE 2014

DOI: 10.1587/comex.3.61
Received January 21, 2014
Accepted January 28, 2014
Published February 20, 2014

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IEICE Communications Express, Vol.3, No.2, 61–67

1

Introduction

Reliable security systems have been recently attracted much attention. Radiowave sensors using existing signals, such as the radio using Frequency
Modulation (FM), Television (TV) broadcast signals and so on, have beenstudied [1]. The method is relatively simple but there is a issue for the detection accuracy [2]. Moreover, since VHF-FM and UHF-TV signals are usedin [1], the variation of the propagation channel from the outdoor to indoor
might be occurred due to the change in outdoor propagation environment.
In order to solve these problems, an intruder detection using array signal
processing is proposed [2].
In this method, Single Input Multiple Output (SIMO) channel is assumed
and the variation of 1st eigenvector, which is obtained by the correlation matrix of the received signal, is utilized as a cost function of intruder detection.
We proposed an intruder detection method which utilizes channel matrix in
Multiple Input Multiple Output (MIMO) channels [3], in order to enhancedetection performance in [2]. We call this method MIMO Sensor [3]. Since
not only receiving but also transmitting diversity eﬀects are obtained by using MIMO transmission, it was shown that higher reliability for the intruder
detection is obtained by using the MIMO Sensor compared to the SIMO
sensor in an actual indoor environment [3].
The time correlation using the channel matrix has been compared withthat using eignevectors, when 2×2 MIMO system is considered [4]. It isshown that the detection performance by using the channel matrix is almost
same with that by the eigenvectors [4]. Moreover, only results using a raytracing simulation have been shown in [4]. In this letter, the diversity scheme,
which selects multiple eigenvectors according the propagation characteristics,
is proposed : we utilize that the propagation characteristics of 1st and 2nd
eigenvectors in the MIMO channel are diﬀerent with each other [5]. The
eﬀectiveness of the proposed method is clariﬁed by the indoor measurement
when considering a person inside the room.

2

Proposed method

Fig. 1 shows a concept of MIMO sensor using eigenvectors. Fig. 1 (a) and(b) compare the variation of eigenvectors in the MIMO channel due to aperson. M and N are the numbers of transmitting and receiving antennas inFig. 1. V ∈ CM ×M , U ∈ CN ×N are transmitting and receiving eigenvector
matrixes, respectively. As can be seen in Fig. 1, the eigenvectors, V and U
are changed to V  and U  due to the intrusion by the person. We realize theintrusion detection by checking the variation of the channel matrix, V andU.
For simpliﬁcation, let us assume that receiving eigenvectors are considered
and uno,ij (i = 1 ∼ N, j = 1 ∼ L) is a component at the eigenvector without
people in the room. L = min(N, M ). When uij (t) is a component of thec

IEICE 2014

DOI: 10.1587/comex.3.61
Received January 21, 2014
Accepted January 28, 2014
Published February 20, 2014

62

IEICE Communications Express, Vol.3, No.2, 61–67

Fig. 1. Concept of MIMO sensor using eigenvectors.
eigenvector at time t, the time correlation, ρU (t) is represented by




L

N 
∗


u
u
(t)
no,ij ij
 i=1 j=1

ρU (t) = 
.
N L
N L


 


|uno,ij |2 |uij (t)|2i=1 j=1(1)i=1 j=1

We have to judge whether the intrusion is employed or not by using
the value which is obtained in Eq. (1). There are two reasons why Eq. (1)becomes small.
(a) Variation on channel matrix due to the intrusion or surrounding environment(b) Variation on channel matrix due to thermal noise
We evaluate inﬂuence on the time correlation by the thermal noise, because
the inﬂuence by only reason (a) must be detected. The correlation considering
the thermal noise, ρU,w/n ∈ CN ×L is represented as
U w/n = U w/o,n +

1
Un
SNR





L

N 
∗


u
u
w/n,ijwo/n,ij i=1 j=1

ρU,w/n = 
N L
N L

  
2 
uw/o,n,ij  uw/n,ij 2
i=1 j=1c


IEICE 2014

DOI: 10.1587/comex.3.61
Received January 21, 2014
Accepted January 28, 2014
Published February 20, 2014

(2)

(3)

i=1 j=1

where U w/n and U w/o,n are the receiving eigenvector matrixes with/without
the thermal noise, respectively. U w/o,n is ideally assumed to be estimatedreceiving eigenvector matrix.
We obtain U w/o,n with high SNR in the measurement. Since the range of
SNR on the measurement in Sect.4 is 30 to 35 dB, we assume that estimatedchannel matrix H is ideal channel without the noise. Moreover, U w/o,n isobtained by H. Hence, U w/o,n is regarded as the ideal channel without noise.
After the estimation of U w/o,n , we artiﬁcially add the thermal noise inorder to evaluate the performance by the MIMO sensor using the eigenvectors
versus the SNR. U n is the matrix by the thermal noise, which is obtainedby computer simulation after the measurement.
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IEICE Communications Express, Vol.3, No.2, 61–67

uw/n,ij and uw/o n,ij are components on the channel matrix. In this letter,
we used as the threshold value ρU,w/n . Detailed calculation method to obtainthe threshold value is the same when considering the channel matrix [3].
Next, a diversity scheme, which selects multiple eigenvectors according
the propagation characteristics, is proposed. It is well known that 1st eigenvector is generated for the direct wave or wave with the maximum power [5].
On the other hand, eigenvectors except 1st eigenvector are generated for themultipath waves [5]. Hence, the diversity eﬀect, which means the detection
using diﬀerent waves, is expected when using multiple eigenvectors.
Let us consider kth eigenvector, uk ∈ CN ×1 (k = 1 ∼ L). Let us assume
that uk,no,i (i = 1 ∼ N ) is a component at the k eigenvector without people
in the room. When uk,i (t) is a component of the k eigenvector at time t, thetime correlation at k eigenvector, ρU,k (t) is represented by
N





u∗k,no,i uk,i (t)



i=1

.
(4)
ρU,k (t) = 
N
N



|uk,no,i |2 |uk,i (t)|2
i=1

i=1

In the proposed method, the diversity eﬀect can be expected by using thefollowing scheme:• The intruder is judged when at least one of ρU,k (t) (k = 1 ∼ L) is lessthan threshold value.
• The intruder is not judged when all the ρU,k (t) (k = 1 ∼ L) are greaterthan threshold value.
Note that the threshold value must be calculated for each eigenvector, because the SNRs among all the eigenvalues are diﬀerent with each other. Although we explained the receiving eigenvector, the transmitting eigenvectors
can be incorporated into the proposed method in the same way.

3c


IEICE 2014

DOI: 10.1587/comex.3.61
Received January 21, 2014
Accepted January 28, 2014
Published February 20, 2014

Measurement environment

In order to clarify the eﬀectiveness of proposed method, we conducted themeasurement in an actual indoor environment. The measurement environment is shown in Fig. 2. The number of transmitting and receiving antennas
are two and four, respectively. Therefore, two eigenvectors (1st and 2nd) are
obtained. The sleeve antenna, which is known as omni-directional antennain the horizontal plane, is used for the transmit and receive antennas. In thismeasurement, the frequency is 2.55 GHz. MIMO-OFDM signals based on
IEEE802.11n standard are used and detail signal format and conﬁgurationof MIMO OFDM testbed are described in [3]. The array width dTx and dRx
at the transmitter and receiver sites are set to be 1.5 λ (λ : one wavelength).
The positions of transmit antennas were moved 10 times with the intervalof 0.5 λ by using a position controller so as to keep d = 1.5 λ. The antennaheight at Tx and Rx sites are set to be 1.0 m. As shown in Fig. 2, the MIMO
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IEICE Communications Express, Vol.3, No.2, 61–67

Fig. 2. Measurement environment.
channels are measured when a person moves on Courses A and B. Since therange of SNR on the measurement in Sect. 4 is suﬃciently high (30 to 35 dB),
the noise was added in the computer simulation after the estimation of H,
to change the value on SNR.

4c
IEICE 2014

DOI: 10.1587/comex.3.61
Received January 21, 2014
Accepted January 28, 2014
Published February 20, 2014

Person detection performance by eigenvector diversity

First, the detection performance is considered by Courses A and B, in orderto clarify the feature using 1st and 2nd eigenvectors. Fig. 3 (a) shows thedetection probability versus SNR at course B. As can be seen in Fig. 3 (a),the detection performance by 1st eigenvector and channel matrix is higher
than that by 2nd eigenvector. It is observed that the time correlation by 1st
eigenvector is small when the person moves in front of antennas, because 1st
eigenvector detects the directed wave.
Fig. 3 (b) plots the detection probability versus SNR at Course A. As canbe seen in Fig. 3(b), the detection performance by 2nd eigenvector is higher
than that by 1st eigenvector and channel matrix. Since 2nd eigenvector ischanged by the variation of propagation channel which arises by 1st reﬂected
wave, it is conﬁrmed that detection rate by 2nd eigenvector is relatively higher
than that by 1st eigenvector, when the person is away from the antennas.
The diversity eﬀect can be expected by the results in Fig. 3 (a) and (b).
Fig. 3 (c) shows an example of the time correlation of eigenvectors at Course
A. The person is judged when each correlation value by 1st and 2nd eigenvectors is smaller than the corresponding threshold values in Fig. 3 (c). As
can be seen in Fig. 3 (c), the location where the correlation value is smaller
than the threshold value by 1st eigenvector is diﬀerent from that by 2nd
eigenvector.
The detection probability when employing eigenvector diversity is plotted

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IEICE Communications Express, Vol.3, No.2, 61–67

Fig. 3. Detection performance with eigenvectors and effect on eigenvector diversity method.
in Fig. 3 (d). The results on all the Courses A and B are considered inthis ﬁgure. As can be seen in Fig. 3 (d), the detection probability by the
eigenvector diversity is higher than that by channel matrix [3]. When the
SNR is 15 dB, the detection probability using the eigenvector diversity is
96.3%. On the other hand, the detection probability using channel matrix is
only 87.0% with the same SNR. Therefore, it is shown that the eigenvector
diversity is eﬀective for improving the detection rate in the MIMO sensor.

5

Conclusion

In this letter, we evaluated the detection performance on the person by the
MIMO sensor using multiple eigenvectors, when considering the person in theroom. From the measurement results, the time correlation by 1st eigenvector
and channel matrix is small when the person moves in front of antennas,
because 1st eigenvector detects the directed wave. On the other hand, it isveriﬁed that 2nd eigenvector can detect the reﬂected wave because the detection rate by 2nd eigenvector is higher than that by 1st eigenvector in such a
scenario. Moreover, we proposed the diversity scheme which selects multiple
eigenvectors by using the diﬀerent feature between 1st and 2nd eigenvectors.
Finally, it is shown that the eigenvector diversity is eﬀective for improving
the intruder detection performance in the MIMO sensor.
c


IEICE 2014

DOI: 10.1587/comex.3.61
Received January 21, 2014
Accepted January 28, 2014
Published February 20, 2014

66

IEICE Communications Express, Vol.3, No.2, 61–67

Acknowledgments
The part of this work is supported by KAKENHI, Grant-in-Aid for Scientiﬁc Research (C) (25420362) and by the Telecommunication Advancement
Foundation in 2013. The authors would like to thank for the members of
Nishimori Laboratory for the help on the measurement.

c

IEICE 2014

DOI: 10.1587/comex.3.61
Received January 21, 2014
Accepted January 28, 2014
Published February 20, 201467