VOL. E101-B NO. 2
FEBRUARY 2018
The usage of this PDF file must comply with the IEICE Provisions
on Copyright.
The author(s) can distribute this PDF file for research andeducational (nonprofit) purposes only.
Distribution by anyone other than the author(s) is prohibited.

IEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018

400

PAPER

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

RSSI-Based Localization Using Wireless Beacon withThree-Element Array
Ryota TAZAWA†a) , Student Member, Naoki HONMA† , Member, Atsushi MIURA†† ,and Hiroto MINAMIZAWA†† , Nonmembers

SUMMARY In this paper, we propose an indoor localization methodthat uses only the Received Signal Strength Indicator (RSSI) of signals
transmitted from wireless beacons. The beacons use three-element array
antennas, and the position of the receiving terminal is estimated by using
multiple DOD information. Each beacon transmits four beacon signals
with different directivities by feeding signals to the three-element array
antennas via 180-degree and 90-degree hybrids. The correlation matrix ofthe propagation channels is estimated from just the strength of the signals,
and the DOD is estimated from the calculated correlation matrix. For
determining the location of the receiving terminal, the existence probability
function is introduced. Experiments show that the proposed method attains
lower position estimation error than the conventional method.
key words: RSSI, wireless beacon, DOD estimation, localization

1.

Introduction

The satellite geolocation system called Global Navigation
Satellite System (GNSS) is in wide use outdoors. However, it does not work in indoors since the satellite signals
are not available. Indoor localization methods that use radio beacons have been well studied to resolve this problem[1], [2]. The radio beacons must be inexpensive, low-power
consumption, easy-to-install, and capable of handling many
terminals such as smartphones in crowded public spaces. Existing commercially available beacons mainly use BluetoothLow-Energy (BLE), however, only the RSSI of the beacon
signal is available at the receiver. Most localization schemesthat use RSSI [3], rely on either RSSI-ranging [4], [5] orfingerprint techniques [6], [7]. RSSI-ranging simply translates the observed RSSI into distance information by using
a predetermined relationship between propagation loss anddistance. The receiver estimates the distances from three ormore beacons, and deduces its own location, so the locations of all beacons must be known in advance. However,
it is difficult to estimate the distance accurately from just
RSSI values, because multi-path fading is common in indoor environments. The fingerprint technique is known tobe relatively accurate but a database of measured RSSI distributions must be created in advance for each site. The
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 Embedded Resource Integration Inc,
Morioka-shi, 020-0125 Japan.
a) E-mail: t2316029@iwate-u.ac.jp
DOI: 10.1587/transcom.2017ISP0027

position is estimated by comparing the measured RSSI distribution with the created database. However, this methodneeds exhaustive measurements of the RSSI values throughout over the designated site, and a change in the propagation
environment necessitates regeneration of the RSSI database.
Fortunately, localization techniques based on Direction-ofDeparture (DOD)/Direction-of-Arrival (DOA) information
can well estimate the position of the target even when thesignal strength does not correspond to the distance due tothe multipath fading [8]. This approach commonly uses array antennas, and the DOD/DOA information is calculated
by using array signal processing. Especially in line-of-sightenvironments, it can accurately estimate the position fromthe multiple DOD/DOA information by triangulation. Moreover, this method does not require a pre-measured database.
However, the fatal problem of DOD/DOA based technique isthat this technique requires phase information of the signals,
which is not available in currently available beacons. The
authors have studied a DOD estimation method that uses only
the RSSI to solve this problem [9]. In this method, a signal
pre-processing technique that uses an analog circuit is introduced for reconstructing the phase information from RSSI
values. The analog feed network comprising 180-degree and90-degree hybrids is configured at the beacon side, and the
beacon signals pass through the feed network and are transmitted by a two-element array antenna. Physically speaking,the hybrids are used for forming four unique radiation patterns. The receiver observes the RSSIs of these four signals
and calculates its own direction from the beacon array by using the strength differences among these signals. However,the location accuracy of this method is not sufficient because
the transmitter array has only two elements. In this paper,
the DOD-based localization method using three-element arrays at the beacon transmitter side [10] is experimentallyinvestigated in detail. The accuracy of DOD estimation is
improved by using the three-element array; the number of
the transmitters used at each beacon, four, equals the number in our previous work [9]. Also,the estimation accuracy
is improved by using the statistical approach for determining
location. A measurement campaign is carried out in an actual indoor environment to evaluate the performance of the
proposed method in a comparison with the RSSI-ranging
method; study [10] provided only DOD-based results.

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

TAZAWA et al.: RSSI-BASED LOCALIZATION USING WIRELESS BEACON WITH THREE-ELEMENT ARRAY401

h90−2 =

1
( j h2 + h3 ),2(5)where the 3 dB loss of the combiners is taken into account.
In this system model, the power supplied from the beacon
transmitters to each input port is assumed to be identical andknown. Therefore, the gain of this channel can be directly
determined from the RSSI. The gain of the observed channel
is represented by,
Fig. 1

Proposed system configuration.

1(|h1 | 2 + |h2 | 2 + h1 h2∗ + h1∗ h2 )41|h180−2 | 2 = (|h1 | 2 + |h2 | 2 − h1 h2∗ − h1∗ h2 )412|h90−1 | = (|h2 | 2 + |h3 | 2 − j h2 h3∗ + j h2∗ h3 )412|h90−2 | = (|h2 | 2 + |h3 | 2 + j h2 h3∗ − j h2∗ h3 ).
4|h180−1 | 2 =

2.

2.1

Proposed RSSI-Based DOD Estimation and Localization
DOD Estimation by Using RSSI

Figure 1 shows the proposed system configuration. Fourbeacon signals are transmitted from the three-element array antenna connected to 180-degree and 90-degree hybrids.
Transmission antennas#1 and #2 are connected to the 180
degree hybrid, while transmission antennas #2 and #3 areconnected to the 90 degree hybrid. The 180 degree hybrid
is used to form sum and differential patterns, and the 90
degree hybrid is used to form right- and left-direction patterns. This means four radiation patterns are created andare treated as individual beacon signals. To allow the four
beacon transmitters to share the three-element array, threecombiners are used to connect the feed networks to the array.
One out of two input ports of the combiners at both sidesof the array is terminated to keep the excitation amplitudes
equal. The DOD is calculated at the receiver side by the proposed phase-reconstruction technique described below. h1 ,h2 , and h3 represent the propagation channels from the transmitting array to the receiving antenna. h180−1 , h180−2 andh90−1 , h90−2 represent the observed channel passed throughthe feed network and so include the characteristics of the 180degree, 90-degree hybrids. The correlation matrix definedby the propagation channel is expressed as,
R11 R12 R13
R = h H h = *. R21 R22 R23 +/, R31 R32 R33 |h1 | 2 h1∗ h2 h1∗ h3*= . h1 h2∗ |h2 | 2 h2∗ h3 +/∗∗2, h1 h3 h2 h3 |h3 | -(1)and complex conjugate, respectively. Since the channel
passes through the feed network with its hybrids and the
combiners, the observed channel is expressed as,1(h1 + h2 )21h180−2 = (h1 − h2 )21
h90−1 = (h2 + j h3 )
2

(7)
(8)
(9)

A correlation matrix can be approximated from (6)–(9) by
the following procedure. Since the relation R21 = R12 ∗ issatisfied, the gain difference between the sum and differential
ports yields,
11(h1 h2∗ + h1∗ h2 ) = (R12 + R21 )2
2= |R12 | cos α(10)|h180−1 | 2 − |h180−2 | 2 =where α represents the phase of R12 . The sum of the gains
is calculated as,1(|h1 | 2 + |h2 | 2 ) ≥ |h1 h2 |,2= |R12 |(11)|h180−1 | 2 + |h180−2 | 2 =where the inequality of arithmetic and geometric means isused. Note that the equality of (11) holds when and only
when |h1 | 2 = |h2 | 2 . If the transmitting array and receiving antenna are in line-of-sight (LOS), the propagating distance is sufficiently long, and the multipath component isneglected, |h1 | 2 = |h2 | 2 holds. Hence, α is approximated as,!
2
2−1 |h180−1 | − |h180−2 |.
(12)
α ' ± cos|h180−1 | 2 + |h180−2 | 2
The approximation |h1 | 2 ' |h2 | 2 also simplifies the relationof the correlation matrix elements (1) as,

where {·} H and {·}∗ represent complex conjugate transposeh180−1 =

(6)

(2)
(3)
(4)

|R11 | ' |R12 | ' |R21 | ' |R22 |' |h180−1 | 2 + |h180−2 | 2 = A,

(13)

where A is a real constant that is defined for simplicity.
Therefore, the correlation matrix elements related to Tx #1
and #2 are approximately estimated as,!
!
R11 R12
A
Ae jα
=
(14)
R21 R22Ae−jαA
Similarly, the correlation matrix elements involved in Tx antennas #2 and #3 are determined by the following procedure.
Antennas #2 and #3 are fed through the 90-degree hybrid.
From the relation of (8) and (9), the gain difference between

IEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018402

the 90-degree hybrid ports, h90−1 and h90−2 , yields,
1
1(− j h2 h3∗ + j h2∗ h3 ) = ( j R23 − j R32 )22= −|R23 | sin β(15)|h90−1 | 2 − |h90−2 | 2 =where β represents the phase of R23 . The sum of the gains
is calculated as,1(|h2 | 2 + |h3 | 2 ) ≥ |h2 h3 |2
= |R23 |.

|h90−1 | 2 + |h90−2 | 2 =(16)

As in (11), the approximation, |h2 | 2 = |h3 | 2 , yields |h90−1 | 2 +|h90−2 | 2 ' |R23 |, which leads to!
|h90−1 | 2 − |h90−2 | 2,
β ' sin−1 −|h90−1 | 2 + |h90−2 | 2!
|h90−1 | 2 − |h90−2 | 2.
(17)
π − sin−1 −|h90−1 | 2 + |h90−2 | 2
Also, the correlation matrix elements related to Tx #2 and#3 satisfy the relation of,|R22 | ' |R23 | ' |R32 | ' |R33 |' |h90−1 | 2 + |h90−2 | 2 = B,

(18)

where B is a real constant, too. Therefore, the correlation
matrix elements related to Tx #2 and #3 are approximatedas,!
!
R22 R23
B
Be jβ
=
.
(19)
R32 R33Be−jβ
B
As discussed above, each of the angles, α and β, has two
solutions, where only one of the two is true. Note thath1 h2∗ = h2 h3∗ holds in the ideal environment, where only asingle plane wave exists and there is no multipath component.
By considering (1), α and β represent the phase difference
between the paths of the adjacent antennas, and they should,
in theory, be identical. Therefore, the true solutions of αand β will agree whereas the false solutions do not. Hence,the selection method is quite simple, and consists of justchoosing the set of α and β with the smallest difference.
Component R13 physically represents the signal correlation
between the transmitting antennas, #1 and #3, and can beapproximated as,
R12 R23
R13 ' |R23 |e j (α+β) =R11
ABe j (α+β)== Be j (α+β)A(20)

where the relation, |h1 | ' |h2 | ' |h3 | is assumed. Thus, allelements of the correlation matrix can be derived from justthe strength information. The estimated correlation matrix
is expressed as,
A
R = *. Ae−jα−j (α+β)
, Be

Ae jα
A
Be−jβ

Be j (α+β)
Be jβ +/
B-

(21)

Fig. 2

Simulation model.

Subsequently, DOD can be easily calculated by any of thecommonly used algorithms, such as MUSIC (MUltiple SIgnal Classification) method[11]. We simulated a simple
model in order to clarify the performance of the proposed
method. The simulation results clarified the performance
improvement over the two-element array. We also compared
the performance with an ideal three-element array, i.e. DOD
with phase information. Figure 2 shows the model simulated.
In this figure, Txs represent an M-element transmitting linear
antenna, and Rx represents a one-element receiving antennathat is placed sufficiently far from the Txs; θ is the angle ofsignal transmission, and d is the distance between antenna
elements. All the antenna elements were assumed to be ideal
point wave sources (i.e. omni-directional). The channel vector of propagation is written as,
h = h 0 + h w,

(22)

where h 0 is LOS (Line-Of-Sight) component, which is represented as,h0
h 0 = √ [1, e jkdsinθ , . . . , e jkd(M−1)sinθ ],M(23)where h0 represents the coefficient of strength of the channel
vector, and k represents the wave number. h w represents
the NLOS (Non-Line-Of-Sight) component, each element of
which is assumed to be a random complex vector following a
Gaussian2 distribution. The Rician factor was calculated fromk = | h 0 | 2 . From the vector channel and (1), the correlation|hw |matrix is written as,
R = h H h.

(24)

We then evaluated the angular estimation error achieved with
the MUSIC method. In this simulation, the value of θ isassumed to be −45◦ –45◦ ,and d is set to a half-wave length,
the Rician factor is k = 10, and the number of trials is 10000.
Figure 3 shows the CDF (Cumulative-Distribution-Function)of the angular estimation error yielded by the simulation. In
this figure, w/ phase represents the results of two-element andthree-element arrays with knowledge of phase information.
From this figure, we find that proposed (M = 3) is lower
accuracy than w/ phase (M = 3), but proposed (M = 3)
has higher accuracy than the two-element array. This resultshows the proposed method offers better accuracy than w/

TAZAWA et al.: RSSI-BASED LOCALIZATION USING WIRELESS BEACON WITH THREE-ELEMENT ARRAY

403

Fig. 3

CDF of angular error (simulation).

Fig. 5

Fig. 4

Concept of proposed localization method.

phase (M = 2) does even though the phase information is not
used.
2.2

Localization by Using Only RSSIThis study introduces an angular probability function, theGaussian function; its maximum is taken to the estimatedDOD. By taking all probabilities into consideration, the location most likely to be true is identified. Figure 4 shows theconcept of the proposed localization method. In this figure,ni represents a normal vector of the i-th array antenna, andθ loc and θ est represent direction from transmitting antenna
Tx #i to point S(x, y) and DOD calculated by the correlation
matrix, respectively. The existence probability, f i , at point
S(x, y) for Tx #i is defined as,!
(θ loc (r STi ) − θ est ) 2
1
f i (r STi , θ est ) = √
exp −
(25)
2σ 2
2πσ 2
where r STi is the direction vector from Tx #i to point S(x, y),and σ 2 represents the variance of the Gaussian function. The
value of σ 2 is arbitrarily set, because we confirmed that there
is no significant change in accuracy when the value of σ 2 ischanged. The probabilities yielded by all Txs are given as,
F (r STi ) =NYf (r STi )

(26)

i=1

where N is the number of Txs. The position at which theexistence probability is maximum in the estimated range
is identified. Therefore, this proposed method is robust
even of some of the DOD estimations fail. Figure 5 shows
the flowchart of the proposed localization method. In the
flowchart, ‘Correlation matrix estimation’ represents calculation of the correlation matrix shown in (1)–(21) at Rx.

Flowchart of proposed localization method.

‘DOD estimation (MUSIC)’ represents the angular estimation by applying the MUSIC method to the estimated correlation matrix. ‘Angular probability function is introduced to
DOD information’ represents the application of the angularprobability function as shown in (25). ‘Multiplication of existence probability’ represents the multiplication of existence
probability as shown in (26). Finally, ‘Maximum probability
point search’ represents the search of the position of maximum existence probability in the estimation range. Note thatthe proposed method assumes the locations and orientationsof all Txs are known in advance.
3.

Experimental Conditions and Environment

Figure 6 shows the experimental environment, and Fig. 7
shows the experimental setup. This experiment was carriedout in an indoor environment, the size of which was 14 m ×
10 m; the operating frequency lay in the 2.4 GHz band. It is
obvious that this environment is multipath because it is surrounded by concrete walls [12], [13]. The transmitting arrays
were placed at the four corners of the test area, and directed
at the angle of 45◦ with respect to the walls. Measurements
were made at 65 locations (receiving antenna), as shown in
Fig. 6. Figure 8 shows the antennas used in this experiment,
and Fig. 9 shows their measured directivities. The sleeveantenna and the patch antennas were designed with a center
frequency of 2.47125 GHz. The sleeve antenna was usedat the receiving side. Each transmitting array consisted ofthree-element patch antennas, where the inter-element spacing was one half the wavelength of 2.47125 GHz. Antennaheight was 1.0 m. In the measurement regime, all Tx antennas were alternately fed through an RF switch, and complex
channels between the Rx antenna and all Tx antennas were
measured. RSSI values were determined by multiplying
the absolute value of the complex channel by the assumed
transmission power. We did not use an actual hybrid circuit in this experiment, and only a basic evaluation of the
proposed method was attempted. Hence, we post-processed

IEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018404

the gathered data. First, we measured the complex channel
between the feed port of the antenna and the receiving antenna. Second, the channel through the feed network using
ideal hybrids was estimated using by the cascade connection
technique using S-parameter [14].
4.

Fig. 6

Experimental environment.

Fig. 7

Experimental setup.

Fig. 8

Photos of Tx and Rx antennas.

Fig. 9Directivities of antennas used.

Measurement Results

Four other localization methods were used in assessing theperformance of the proposed method. One was the DODbased localization method; an enhancement of the method ofreference [9], that uses the likely-hood approach describedin Sect. 2.2, and a 90-degree hybrid is used in addition to
the 180-degree hybrid. Figure 10(a) shows the concept of
this method with two antenna elements. Four beacon signals
are transmitted by the two antenna elements by passing thesignal through 90-degree and 180-degree hybrids. The output signals from the two hybrids are combined just before
the antenna feed ports. In addition, the DOD-based localization method using two-element array and three-element
array with phase information were used to compare the performance of proposed localization. And also, a likely-hood
approach described in Sect. 2.2 is introduced to these localizations with phase information. The MUSIC algorithm
was used to estimate the DODs in the proposed and conventional DOD-based methods. The other method used the
RSSI-ranging algorithm. Figure 10(b) shows the conceptof localization by translating RSSI to distance. The circles
indicate the estimated distances, and the receiver position
should be lie the intersection of the circles. However, theintersection points are not precise due to distance estimation
error. In this study, location is estimated by averaging allintersection points.
Figure 11 shows the distributions of the RSSI corresponding to the four Txs. It can be seen that the RSSI
attenuates with distance from Tx, but the degree of the attenuation strongly depends on the direction from Tx. Figure 12
plots the relation between propagation distance and RSSI,
which is calculated from the received power and the distance
between receiving antenna and transmitting antennas in theenvironment see Fig. 6. Figure 12 shows the relation between
RSSI and propagation distance, D, which is the distance between the transmitting and receiving antennas. Figure 12
shows that the received power falls as the distance increases.
The position is estimated by averaging all locations of the in-Fig. 10

Concept of conventional localization.

TAZAWA et al.: RSSI-BASED LOCALIZATION USING WIRELESS BEACON WITH THREE-ELEMENT ARRAY

405

Fig. 11

Fig. 12Fig. 13

Fig. 14RSSI distributions with four Txs (h180−1 ).

Fig. 15RSSI distributions with four Txs (h180−2 ).

RSSI distributions with four Txs.

Received signal power vs propagation distance.

Directivities of antennas used with circuit.

tersections within the estimation range. Figure 13 shows thefour antenna directivities of used antennas with the proposed
circuit. We calculated four antenna radiation patterns by ap-

plying the weight generated by the ideal proposed circuit
to the measured antenna patterns. Figure 14–17 shows the
distribution of the RSSI of the proposed method, where four
distributions are depicted for each beacon location because
four different signals are transmitted with four radiation patterns. From these figure, it can be seen that the proposed
feed network transmits signals with four different antenna directivities. Figure 18 evaluates the angular estimation error
with respect to amplitude difference. This figure shows how
much the angular estimation error is affected by the amplitude difference between elements due to multipath fading;amplitude difference between elements is calculated as thevariance (σ a2 ). From figure, it can be seen that the angularestimation error increases with variance, and we confirmed

IEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018406

Fig. 18

Fig. 16

Evaluation of angular estimation error vs amplitude difference.

RSSI distributions with four Txs (h90−1 ).

Fig. 19

Fig. 17

CDF of angular error.

RSSI distributions with four Txs (h90−2 ).

that the 90 percentile value of angular estimation error does
not exceed 8.90 degrees. Figure 19 shows CDF of the angular errors of the direction estimates. The proposed method
was compared to the conventional method, i.e. the DOD
estimation technique using two-element array antennas thatshown in Fig. 10(a). The proposed method is also comparedto the DOD estimation techniques, where two-element methods with and without phase information and three-element
method with phase information are used. The measured tendency well agrees with the simulation results shown in Fig. 3.
The 50 percentile values of the error in the proposed method
and the three-element array method using phase information were 3.9◦ , 3.5◦ , respectively. And, the 50 percentile
values of the error in both of the conventional two-element

Fig. 20

Example of proposed localization.

array method shown in Fig. 10(a) and the two-element array
method using phase information were 5.6◦ . Although theproposed method is lower accuracy than the three-element
array with phase information, it is found that the proposed
method improves the DOD estimation error by 1.7◦ compared to the conventional two-element method. Figure 20
shows the probability distribution calculated from the measured RSSI by using (26), where the Rx is located at point

TAZAWA et al.: RSSI-BASED LOCALIZATION USING WIRELESS BEACON WITH THREE-ELEMENT ARRAY

407

estimated position when the value of σ 2 in (25) was varied.
Figure 22 shows that the estimation performance is constantregardless of σ 2 . These results confirm that the proposal
to use three-element array antennas offers highly accurate
localization even in multipath indoor environments.
5.

Fig. 21

Fig. 22

CDF of distance error.

CDF of distance error for various σ 2 values.

31, i.e. (X, Y ) = (4.5, 8.5). The lines indicate the DOAs
estimated by the proposed method. Even though some of theesitimated DODs have slight angular error, the probability
maximum appears around the actual location. The distance
between the maximum point and actual location is 0.63 m inthis case. Figure 21 shows CDF of the distance errors of theposition estimates. In addition to the DOD-based methods,
the localization result of the RSSI-ranging-based technique isalso plotted. From this figure, it can be seen that it is difficult
to estimate the position by the RSSI-ranging method since
the RSSI is not directly proportional to distance as shownin Fig. 12. This yields several false images and thus large
error in localization. Also, the proposed method was compared to the conventional method, i.e. the DOD estimation
technique that uses two-element array antennas that shownin Fig. 10(a). In addition, it was compared to the DOD estimation technique using two-element array and three-element
array with phase information. The 50 percentile values ofthe error in the proposed method and the three-element array
method using phase information were 0.7 m, 0.5 m, respectively. And, the 50 percentile values of the error in both of theconventional two-element array method shown in Fig. 10(a)and the two-element array method using phase information
were equally 0.9 m. Although the proposed method is lower
accuracy than the three-element array with phase information, it is found that the proposed method improves the position estimation error by 0.2 m compared to the conventional
method. Figure 22 shows the CDF of the distance error in theConclusion

This paper has proposed an improved indoor localization
method that uses the DOD information calculated from the
RSSI values of signals transmitted from three-element array
antennas. This method uses multiple beacon signals, each ofwhich are generated from three-element antennas connected
to four beacon transmitters by way of 180-degree and 90degree hybrids. Experiments showed that the proposed DOD
estimation method improves the median angular error by
1.7◦ compared to the conventional method. Moreover, theaccuracy of the proposed method with three-element array
was 0.7 m (50 percentile), whereas the accuracies of thetwo-element method and RSSI-ranging-based method were
0.9 m and 2.7 m, respectively (50 percentile). These results
indicate that the proposed method with three-element arrays
at the beacon side offers the best accuracy among the methods
examined in this study. We are currently studying the useof actual hybrid circuits, and the accuracy of the position
estimates yielded with actual circuits will be announced in a
future paper.
Acknowledgments
This work was supported in part by JSPS KAKENHI Grant
Number 16K14262.
References[1] M.L. Sichitiu and V. Ramadurai, “Localization of wireless sensor networks with a mobile beacon,” Proc. 1st IEEE International
Conference on Mobile Ad-hoc and Sensor Systems (MASS 2004),
pp.174–183, Oct. 2004.
[2] S.S. Chawathe, “Beacon placement for indoor localization using
bluetooth,” 11th International IEEE Conference on Intelligent Transportation Systems (ITSC 2008), pp.980–985, Oct. 2008.
[3] P. Barsocchi, S. Lenzi, and S. Chessa, “A novel approach to indoor
RSSI localization by automatic calibratoin of the wireless propagation model,” IEEE 69th Vehicular Technology Conference (VTC
Spring 2009) pp.1–5, April 2009.
[4] S. Maddio, A. Cidronali, and G. Manes, “RSSI/DoA based positioning systems for wireless sensor network,” in New Approach of Indoor
and Outdoor Localization Systems, pp.139–162, InTech, 2012.
[5] A. Bose and C.H. Foh, “A practical path loss model for indoor wifi
positioning enhancement,” 6th International Conference on Information, Communications & Signal Processing, pp.1–5, Dec. 2007.
[6] K. Kaemarungsi and P. Krishnamurthy, “Modeling of indoor positioning systems based on location fingerprinting,” Proc. 2004 IEEE
International Conference on Computer Communications (INFOCOM 2004), vol.2, pp.1012–1022, March 2004.
[7] M. Bshara, U. Orguner, F. Gustafsson, and L. Van Biesen, “Fingerprinting localization in wireless networks based on received-signalstrength measurements: A case study on WiMAX networks,” IEEE
Trans. Veh. Technol., vol.59, no.1, pp.283–294, Jan. 2010.
[8] Z. Shan and T.-S.P. Yum, “Precise localization with smart antennas

IEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018408

in ad-hoc networks,” Proc. 2007 IEEE Global Telecommunications
Conference (GLOBECOM 2007), pp.1053–1057, Nov. 2007.
[9] N. Honma, K. Ishii, and Y. Tsunekawa, “DOD-based localization
technique using RSSI of indoor beacons,” Proc. 2015 Internatinal
Symposium on Antennas Propagatation (ISAP 2015), S4.11, pp.923–
924, Nov. 2015.
[10] R. Tazawa, N. Honma, M. Atsusi, and M. Hiroto, “Improving accuracy of RSSI-based indoor localization using three-element array,” Proc. 2016 Internatinal Symposium on Antennas Propagatation(ISAP 2016), POS1-26, pp.336–337, Oct. 2016[11] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. Antennas Propag., vol.34, no.3, pp.276–280,
March 1986.
[12] T. Wysocki and H.-J. Zepernick, “Characterisation of the indoor
radiopropagation channel at 2.4 GHz,” J. Telecommun. Inf. Technol.,vol.1, no.3-4, pp.84–90, 2000.
[13] E. Walker, H.-J. Zepernick, and T. Wysocki, “Fading measurements
at 2.4 GHz for the indoor radio propagation channel,” IEEE International Zurich Seminar on Broadband Communications, pp.171–176,
Feb. 1998.
[14] G.R. Simpson, “A generalized n-port cascade connection,” Proc.
IEEE MTT-S International Microwave Symposium Digest, vol.81,
no.1, pp.507–509, June 1981.

Ryota Tazawa
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 indoor localization using three-element array antenna.

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.

Atsushi Miura
graduated from Takasaki
City University of Economics in 2000. He joined
ERI Corporation in 2006 and currently works as
a project manager.

Hiroto Minamizawa
graduated from Iwate
Industrial Technology Junior College in 2010.
He joined ERI Corporation in 2010 and currently
works as an electronic circuit designer.