IEICE TRANS. COMMUN., VOL.E96–B, NO.10 OCTOBER 2013

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PAPER

Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP2012

Intruder Detection Performance of SIMO and MIMO Sensors with
Same Number of Channel Responses
Keita USHIKI† , Student Member, Kentaro NISHIMORI†a) , Senior Member, Naoki HONMA†† ,and Hideo MAKINO†† , Members

SUMMARY
Intruder detection method by utilizing a time variation ofMultiple Input Multiple Output (MIMO) channel (MIMO Sensor) has been
proposed. Although the channel capacity on the MIMO transmission isseverely degraded in time variant channels, we can take advantage of thisfeature in MIMO Sensor applications. We have already demonstrated theeﬀectiveness of 2×2 MIMO sensor using 2.4 GHz band at a small room
(Size is 50 m2 ). In this paper, we compare the detection probability of
SIMO/MIMO sensors when the number of channel responses are same between SIMO/MIMO sensors: The numbers of transmit and receive antennas are 1 and 4 (SIMO), it is clarified that 2 and 2 (MIMO). The measurement was carried out at the room with the size of 140 m2 . From themeasured results, 2×2 MIMO sensor obtains the same or higher detection
probability compared to 4×1 SIMO sensor regardless of the measured location.
key words: MIMO Sensor, SISO sensor, time variant channel, channel
matrix1.

Introduction

Reliable security systems have been recently attracted muchattention. Radiowave sensors using existing signals, suchas the radio using Frequency Modulation (FM), Television(TV) broadcast signals and so on, have been studied, because the radiowave sensors can detect the signal even in
Non Line of Sight (NLoS) environment unlike conventional
infrared light and camera for security usage.
The radiowave sensors by using UWB-IR and received
signal strength indicator (RSSI) based detection method etc.
are proposed [1], [2]. The former method needs delay profile measurement and wideband signals are required, hence,the system becomes very complex. The latter method is relatively simple but there is a issue for the detection accuracy[3]. Moreover, since VHF-FM and UHF-TV signals areused in [2], the variation of the propagation channel fromthe 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 [3], [4]. In this method, Single Input Multiple Output (SIMO) channel is assumed and the variationof 1st eigenvector, which is obtained by the correlation matrix of the received signal, is utilized as a cost function ofManuscript received January 21, 2013.
Manuscript revised May 19, 2013.
†
The authors are with the Faculty of Engineering, Niigata University, Niigata-shi, 950-2102 Japan.
††
The authors are with the Faculty of Engineering, Iwate University, Morioka-shi, 020-8551 Japan.
a) E-mail: nishimori@m.ieice.org
DOI: 10.1587/transcom.E96.B.2499

intruder detection.
We proposed an intruder detection method which utilizes channel matrix in Multiple Input Multiple Output(MIMO) channels [5], in order to enhance detection performance in [3]. We call this method MIMO Sensor [6].
Although the channel capacity on the MIMO transmission
is severely degraded in time variant channels [7], we cantake advantage of this feature in MIMO Sensor applications.
Since not only receiving but also transmitting diversity effects are obtained by using MIMO transmission, higher reliability for the intruder detection is expected by using the
MIMO Sensor compared to the SIMO sensor. We proposed
an use of time correlation function for channel matrix in the
MIMO channel [6]. The time correlation using the channel matrix has been compared with that using eignevectors,
when 2x2 MIMO system is considered [8]. It is shown thatthe detection performance by using the channel matrix is
almost same with that by the eigenvectors [8]. Since the calculation complexity using the channel matrix is obviously
small compared to that using the eigenvector, we adopt theuse of time correlation with the channel matrix in MIMO
channel in this paper.
In this paper, we evaluate the following topics to clarify
the further possibility of SIMO/MIMO sensors when considering the distributed antenna configuration.
• 4×2 MIMO-OFDM testbed, which has four receivers
and two transmitters, is developed. We focus on thedetection performance of SIMO/MIMO sensors whichhave same number of channel responses: 4×1 SIMO
and 2×2 MIMO sensors are compared.
• The detection performance by not only 2.4 GHz band
but also 400 MHz band is clarified, to show the possibility of lower frequency band.
• The detection performance is evaluated at a large room
(size is 140 m2 ), to clarify the further possibility of
SIMO/MIMO sensors.
The rest of the paper is organized as follows. In Sect. 2,
the proposed method, which utilizes the variation on thechannel matrix in MIMO channel, is shown. Sect. 3 describes the detailed measurement environment and MIMOOFDM testbed. In Sect. 4, we compare the performance by
SIMO and MIMO sensors when considering a large room
(size is 140 m2 ). Finally, Sect. 5 concludes the paper.

c 2013 The Institute of Electronics, Information and Communication Engineers
Copyright IEICE TRANS. COMMUN., VOL.E96–B, NO.10 OCTOBER 2013

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2.

Principle of MIMO Sensor

Figure 1 shows a concept of MIMO sensor [6]. Figures 1(a)and (b) compare the variation of channel matrix in the
MIMO channel due to a person. Although the channel capacity of MIMO transmission is severely degraded in thetime variant channels [7], we utilize the variation of channel
matrix in MIMO channel as an input of Sensor. When M
and N are the numbers of transmitting and receiving antennas, the channel matrix H ∈ CN×M is change to H ∈ CN×M
due to the intrusion by the person. We realize the intrusion detection by checking the variation of the channel matrix, H. The variation of the channel matrix can be expressed by time correlation function. Let us assume that
hno,i j (i = 1 ∼ N, j = 1 ∼ M) is a component at the channel
matrix without people in the room. When hi j (t) is a component of the channel matrix at time t, the time correlation,
ρH (t) is represented by


M
N 



h∗no,i j hi j (t)

i=1 j=1.
(1)
ρH (t) = 



 N M
 N M






h 2h (t)2
no,i ji=1 j=1ij

i=1 j=1

When using SIMO sensor, the number of transmit antenna, M is set to be 1 in Eq. (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 ρH,w/n
is represented as
1
Hw/n = Hw/o,n +
Hn ,
SNR


M
N 



h∗w/o,n,i j hw/n,i j 
i=1 j=1
ρH,w/n = 
,



 N M
 N M




2
2
h

h

w/o,n,i j 
w/n,i j 
i=1 j=1

Concept of MIMO sensor (Tx: Transmitter, Rx: Receiver).

(3)

i=1 j=1

where Hw/n ∈ C
and Hw/o,n ∈ CN×M are the channel
matrix with/without the thermal noise, respectively. Hn ∈CN×M is the matrix by the thermal noise. hw/n,i j and hw/o n,i j(i = 1 ∼ N, j = 1 ∼ M) are components on the channel
matrix.
Since it is diﬃcult to obtain the ideal channel matrix
without the thermal noise, the channel matrix is estimatedwith the suﬃcient number of samples when considering thestatic channel without the person inside the room in advance. We regard this estimated channel matrix Hw/o,n . After the estimation of Hw/o,n , we artificially add the thermal
noise in order to evaluate the performance by the proposed
method versus the SNR. In the measurement, we confirmedthat actual SNR for Hw/o,n is greater than 30 dB and thischannel matrix is regarded as the ideal channel matrix.
Actual calculation method using Eqs. (1)∼(3) is explained as follows.
N×M(a) First,the calculation of Eq. (1) is employed, and the
SNR is obtained by using the channel matrix, H.
(b) ρH,w/n is calculated by using Eqs. (2) and (3) and SNR
which is obtained in Step (a). Since there is Gaussian
distribution regarding each component of Hn , there isa distribution on ρH,w/n . The values of Hn can be calculated in advance.
(c) ρH,w/n is calculated for each Hn , and x% value of cumulative distribution function (CDF), ρH,w/n (x%, SNR) is
obtained.
(d) The person is judged to be detected when the equation
is satisfied asρ MI MO < ρH,w/n (x%, SNR).

Fig. 1(2)(4)

In this paper, ρ MI MO by Eq. (1) is calculated when the person exists inside the various locations of the room, and theprobability where Eq. (4) is satisfied among all the trials isdefined the detection probability. In order to exam how the
proposed method detects the person inside the room, we
evaluate how the person is detected by using Eqs. (1)∼(4)
instead of the intruder detection. Even if Eq. (4) is satisfied,
the false alarm may occur with the probability of x% due tothe eﬀect on the thermal noise. We define this probability as
false detection rate. In this paper, x is set to be 1% as one ofexamples.
In order to obtain the threshold value, ρH,w/n (x%,USHIKI et al.: INTRUDER DETECTION PERFORMANCE OF SIMO AND MIMO SENSORS WITH SAME NUMBER OF CHANNEL RESPONSES

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Fig. 3Fig. 2 Example of threshold value of time correlation versus SNR when
considering only thermal noise.

SNR), Hw/o,n and Hn are required as shown in Eqs. (2) and(3). When considering the channel matrix without the person in the room, we confirmed that Hw/o,n is not changed[9]. Hence, Hw/o,n can be prepared before the calculation in
Eq. (1). Moreover, Hn can be prepared by using the random
values with Gaussian distribution. Hence, threshold value
can be prepared in advance before calculating Eq. (4).
Figure 2 shows an example of threshold value oftime correlation versus SNR when considering only thermal noise. The false detection probability is 1%. As can beseen in Fig. 2, the correlation is reduced due to thermal noise
when the SNR is very small. On the other hand, the correlation is greater than 0.99 when considering SNR ≥ 12 dB.
We calculate this curve for each measurement and use thiscurve as the threshold for the intruder detection.

Table 1 Selected antennas.
Tx1
Tx2
Rx1
Rx2
Rx3
MIMO
SIMO (1)
SIMO (2)◦◦Fig. 4

3.

Measurement environment.

◦
◦

◦
◦
◦

◦
◦

◦
◦

Rx4
◦
◦
◦

Antenna arrangement.

Measurement Environment

To compare the SIMO/MIMO sensors with same numberof channel responses, we conducted the measurement in an
actual indoor environment. The measurement environmentis shown in Fig. 3. The size of room is 8.9 × 15.8 × 2.6 m(140 m2 ) in X, Y and Z planes. This room is almost triple
size of that in evaluated [6]: SIMO/MIMO sensors are compared at such a large room. During the measurement, thetransmitter and receiver are fixed at Line-of-sight (LOS) position. The number of transmit and receive antennas are two
and four, respectively: 4×2 MIMO channel measurement
can be realized. In this paper, we focus on the detection performance of SIMO/MIMO sensors which have same number of channel responses: SIMO with (M, N) = (1, 4) andMIMO with (M, N) = (2, 2). Table 1 shows the selected antennas for SIMO and MIMO sensors. Although there is fourantennas at the receiver, antennas #1 and #4 are selected forthe evaluation on the distributed array.
The print dipole antenna, which is known as omnidirectional antenna in the horizontal plane, is used for thetransmit and receive antennas [10]. As shown in Fig. 3,
the MIMO channels are measured when a person moves
on Course A, B, C, D, E and F. The total measurement
time were 12 seconds for courses A to D and 8 seconds forcourses E and F, respectively. The number of human subject is two and their body height is approximately 170 cm.
The antenna positions for Tx and Rx in this measurement are
shown in Fig. 4. The array width dTx and dRx at the transmitter and receiver sites are set to be 5.4 m, for the evaluationon the distributed array configuration. d = dTx = dRx . The
positions of transmit antennas were moved 10 times with theinterval of 6.25 cm by using a position controller. So as tokeep d = 5.4 m. The antenna height at Tx and Rx sites are
set to be 1.0 m.
In this paper, the detection performance is also compared due to the diﬀerence of the frequency band. We selected not only 2.4 GHz band [6] but also 400 MHz band,
because 400 MHz band might be used in RF-ID or sensor
systems. Moreover, there is a possibility to utilize high SNR
by 400 MHz band. On the other hand, the antenna size by
400 MHz band is larger than that by 2.4 GHz band. In order to fairly compare the performance by these frequency
band (2.4 GHz/400 MHz), the antenna width in Fig. 4 isfixed to be 5.4 m.
Figure 5 shows MIMO transceiver which is used in thismeasurement. The bandwidth is 6.25 MHz, respectively.
The transmit power was changed according to the measure-IEICE TRANS. COMMUN., VOL.E96–B, NO.10 OCTOBER 20132502

ment. The signal format at the transmitter is shown in Fig. 6.
We adopt OFDM transmission which is incorporated in WLAN systems [11], because W-LAN is widely incorporated
in indoor environment and W-LAN signals might be used
not only for the communication but also the intruder detection by the proposed method. The main parameters intransceiver and OFDM transmission are denoted in Table 2.
The timing synchronization is employed by using the shortpreamble (SP in Fig. 6). We obtained the channel matrix byusing long preamble (LP in Fig. 6) of OFDM signals with
the interval of 0.152 ms. The correlation value is obtainedin each subcarriers of OFDM signals by using Eqs. (1) and(3).

Fig. 5

4.

Intruder Detection by SIMO/MIMO Sensors

4.1 Correlation Characteristics with/without People
In this subsection, basic performance of 2×2 MIMO sensor
is clarified. Hera, the measured frequency is 440 MHz. In
order to confirm reliability of this measurement, the time
variation of the channel matrix without people in the roomis plotted in Fig. 7. Although there is not the person inside
the room, there is the person at the corridor near the room in
Fig. 7. As can be seen in this figure, since time correlation isgreater than 0.9995 (equivalent SNR can be regarded greaterthan 33 dB), we can judge that the propagation environment
can be regarded static when considering without the intrusion by people. Hence, we evaluate how the channel matrix is changed with and without the person inside the room
instead of the intruder detection performance hereafter, because the channel matrix is not changed outside the room
even if there is the person very near the room.
Figure 8 shows the CDF versus the time correlation
with and without people in the room. Equations (1) and (3)are used to obtain the CDF of the time correlation for withand without people, respectively. The measured results con-

Transceiver for SIMO and MIMO sensors.

Fig. 7

Fig. 6

An example of time correlation without people.

Signal format at transmitter.

Table 2

Parameters in transceiver and OFDM transmission.
Frequency
2.4 GHz/400 MHz
Bandwidth6.25 MHz
Sampling rate (A/D)
25 MHzSampling rate (D/A)
25 MHzNumber of over-samples
4
FFT points
64
Number of sub-carriers
56
Number of pilot sub-carriers
4
Number of OFDM symbols
80 symbol
Length of short preamble
2 OFDM symbols
Length of long preamble
2 OFDM symbols
Positions of pilot subcarrier
6, 20, 34, 48

Fig. 8

CDF of time correlation.

USHIKI et al.: INTRUDER DETECTION PERFORMANCE OF SIMO AND MIMO SENSORS WITH SAME NUMBER OF CHANNEL RESPONSES

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Fig. 9 Detection probability vs. false detection rate (All courses,
SNR=20 dB).

sidering all the courses are reflected in Fig. 8. Correlations
due to the thermal noise are obtained changing the SNR in
Eq. (3). As can be seen in Fig. 8, the correlation with peopleis smaller than that without people with a high probability
when the SNR is greater than 10 dB. Hence, the relationshipbetween the detection probability and SNR can be obtained,
when the false detection rate is determined.
Figure 9 represents the detection probability versus
false detection rate. False detection rate is changed from0.01 to 10%. The results are plotted in this figure when the
SNR is 15, 20 and 30 dB, respectively. As can be seen Fig. 9,
the detection probability is changed according to the valueof false detection rate, especially, when SNR=15 dB. The
threshold value as the false detection rate in this paper is set
to be 1% for remaining results.

Fig. 10 Detection probability comparison due to the diﬀerence of radio
frequency.

Fig. 11

Detection probability vs. SNR (All courses).

Fig. 12

Detection probability vs. SNR (Course A).

4.2 Detection Performance between 2.4 GHz and
400 MHz Bands
In this subsection, the detection performance by the diﬀerence of radio frequency band is evaluated. Figure 10 denotes
the detection probability versus the SNR when 2.4 GHz and440 MHz bands are used, respectively. The results with
d =5.4 m are plotted in this figure. As can be seen inthis figure, the much higher detection performance by using 440 MHz band is obtained compared to 2.4 GHz band.
The SNRs which can obtain detection 99% probability are−19 and 0 dBm at 440 MHz and 2.4 GHz bands, respectively. Since the diﬀerence of free space path loss between
440 MHz and 2.4 GHz is approximately 16 dB, the higher
detection performance by 400 MHz band is obtained thanks
to a lower path loss. Hence, the higher detection performance is expected by lower frequency band due to the lower
path loss. Hereafter, we use the antennas whose frequency
band is 440 MHz band.
4.3 Performance Comparison between SIMO and MIMO
Sensors
Figures 11, 12 and 13 compare the detection probabilities

when considering SIMO (M = 1, N = 4) and MIMO (M =2, N = 2) sensors. The results with all courses, course A andcourse D are plotted in Figs. 11, 12, and 13, respectively.
The noise power is given so that the average SNR is 30 dB
when the transmit power is −13 dBm probability at course
A. Hence, the diﬀerence in received power due to antenna
width, measured course and antenna height can be evalu-

IEICE TRANS. COMMUN., VOL.E96–B, NO.10 OCTOBER 20132504

Fig. 13

Detection probability vs. SNR (Course D).

ated. Note that the diﬀerence among MIMO, SIMO (1), andSIMO (2) sensors can be referred in Table 1. As can be seenin Fig. 11, the detection performance by MIMO is slightly
higher than that by SIMO (1) and SIMO (2).
In order to exam the detection performance when considering more realistic antenna arrangement, we evaluatedthe detection performance when the transmit antenna is located at the center on Y-axis of the room in Fig. 3 and its
antenna is also located on the ceiling. The receivers’ setting is the same with SIMO (1) and SIMO (2). We callthis arrangement SIMO (3). The result is also plotted inFig. 11. As can be seen in Fig. 11, the detection rate with
SIMO (3) is much smaller than the other arrangements andthe detection rate is 81% even if SNR=30 dB. In the previous work [6], we have already confirmed that the detection
performance is severely degraded when the antenna heightis higher than body height. We confirmed that the antennaheight strongly aﬀects the detection performance. Hence,
we compare MIMO, SIMO (1), and SIMO (2) hereafter.
On the other hand, as can be seen in Fig. 12, it is shownthat the detection performance by SIMO (1) is much higher
than that by SIMO (2). We also observe that the detection
probability by SIMO (1) is lower than that by SIMO (2), as
shown in Fig. 13.
To clarify the reason why the merit of detection performance by SIMO sensor is changed with respect to eachcourse, the detection rate versus the time of walking is plotted in Figs. 14 and 15 for courses A and D. The SNR is 20 dB
and a person moves with about 1 m per second. The detection probability is calculated with the interval of 1 m. As
shown in Fig. 15, over 80% probability is obtained at almost
every time when considering MIMO and SIMO (1) sensors.
On the other hand, the detection probability is about 60%
with the time of 0 to 6 second when SIMO (2) sensor is applied, because the transmit antenna #1 is located at course
A and the transmit antenna #2 is far from course A. When
we focus on the results in Fig. 15 for course D, the detection probability by SIMO (1) is smaller than that by MIMO
and SIMO (2), because the transmit antenna #2 is located atcourse D and the transmit antenna #1 is far from course D.

Fig. 14

Detection probability vs. measurement time (Course A).

Fig. 15

Detection probability vs. measurement time (Course A).

Table 3 Detection probability due to the antenna composition [%] (SNR
= 20 [dB]).
Course
A
B
C
D
E
F
All

SIMO(1)
91.6
92.2
80.9
82.2
86.5
97.5
88.5

SIMO(2)
73.9
90.9
99.8
96.8
92.8
97.9
92.0

MIMO
93.9
97.6
97.4
95.8
95.5
96.5
96.1

From these results, we confirmed that the detection probability by the SIMO sensor might be degraded due to single
antenna at the transmitter site while the MIMO sensor obtains the high detection probability by both transmit and receive diversity eﬀect: robust performance is expected by the
MIMO sensor.
Table 3 shows the detection probability due to the antenna composition when the SNR is 20 dB.
5.

Conclusion

In this paper, we compared the detection performance of
SIMO/MIMO sensors which have same number of channel responses at a large room (140 m2 ): 4×1 SIMO andUSHIKI et al.: INTRUDER DETECTION PERFORMANCE OF SIMO AND MIMO SENSORS WITH SAME NUMBER OF CHANNEL RESPONSES

2505

2×2 MIMO. It is shown that the detection probability by
4×1 SIMO sensor might be degraded due to single antenna
at the transmitter site while 2×2 MIMO sensor obtains thehigh detection probability by both transmit and receive diversity eﬀect. Moreover, the use of 400 MHz band is effective compared to 2.4 GHz band, because the SNR ismore important than the spatial correlation. Although 1%
is adopted as the value of false detection rate, lower values
should be evaluated as the false detection rate in the future
work for more realizable system.
Although we deal with the time correlation using only
the channel matrix, there may be a change to improve thedetection rate using the eigenvectors when considering thenumber of transmit and receive antennas are greater thantwo, because the multiple eigenvectors are utilized. As the
future work, the eﬀectiveness of the use on the eigenvectors
should be evaluated.
Acknowledgment
The part of this work is supported by KAKENHI, Grantin-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.
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Keita Ushiki
received the B.E. degree fromfaculty of engineering in Niigata University in2012. He is currently studying M.S. degree
at graduate school of science and technology
in Niigata University. His research interest is
MIMO sensors.

Kentaro Nishimori
received the B.E., M.E.
and Ph.D. degrees in electrical and computer engineering form 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 andAssistant Secretary of Technical Committee on Antennas and Propagationof IEICE from June 2008 to May 2010. He received the Young Engineers
Award from the IEICE of Japan in 2001, Young Engineer Award from IEEE
AP-S Japan Chapter in 2001, Best Paper Award of Software Radio Society
in 2007 and Distinguished Service Award from the IEICE Communications
Society in 2005, 2008 and 2010. His main interests are spatial signal processing including MIMO systems and interference management techniquesin heterogeneous networks. He is a member of IEEE. He received IEICEBest Paper Award in 2010.

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, and theBest Paper Award of IEICE Communication Society in 2006, respectively. His current research interest is planar antennas
for high-speed wireless communication systems. He is a member of IEEE.

Hideo Makino
graduated from the Department of Electronic Engineering, Niigata University, in 1976, completed the M.S. program in
1978, and joined the staﬀ of the Department ofInformation Engineering. He became an associate professor in 1990 and a professor in 1995.
He was on leave at the Research Institute of Applied Electrical Engineering, Hokkaido University, in 1983 and at the Medical School, University of Toronto, in 1989. He has been engaged
in research on medical information and assistiveequipment. He holds a D. Eng. degree, and is a member of the Japan ME
Society, the Institute of Electrical Engineers of Japan, and IEEE. He is acouncil member of the Japan Heart Rhythm Society and a councilor of theGeographical Information Systems Society.