=Paper=
{{Paper
|id=Vol-2498/short14
|storemode=property
|title=RSSI-ranging method using pattern diversity and distance-based reliability function
|pdfUrl=https://ceur-ws.org/Vol-2498/short14.pdf
|volume=Vol-2498
|authors=Daichi Kitamura,Naoki Honma,Kazushi Naruke,Atsushi Miura,Yusuke Sugawara
|dblpUrl=https://dblp.org/rec/conf/ipin/KitamuraHNMS19
}}
==RSSI-ranging method using pattern diversity and distance-based reliability function==
https://ceur-ws.org/Vol-2498/short14.pdf
RSSI-Ranging Method Using Pattern Diversity
and Distance-Based Reliability Function
Daichi Kitamura1 , Naoki Honma1 , Kazushi Naruke1 , Atsushi Miura2 , and
Yusuke Sugawara2
1
Graduate School of Engineering
Iwate University
Morioka, Japan 020–8551
G0319051@iwate-u.ac.jp
2
ERi, Inc.
Morioka, Japan 020–0125
miura@erii.co.jp
Abstract. This paper studies the accuracy improvement of the local-
ization technique based on the received signal strength indicator (RSSI)-
ranging using Bluetooth, which has been commonly used for an indoor lo-
calization. In our system, a transmitter diversity technique is introduced,
where a beacon transmits four different ID signals with four different ra-
diation patterns. A smartphone is used as a receiver, and detects the
signal with the strongest RSSI among all beacons. The most likelihood
detection is used to improve the localization accuracy, where the close
beacon is trusted more than the distant one. The experiment revealed
that our diversity scheme improves the average and median localization
errors by 1.22 m and 1.86 m, respectively. The most likelihood detection
considering the distance realizes 6.70 m and 4.62 m accuracy in average
and median values, respectively.
Keywords: RSSI-ranging · Array antenna · Bluetooth.
1 Introduction
The indoor localization techniques using Bluetooth Low Energy (BLE) bea-
cons have been widely studied [1]. For this application, the beacons must be low
cost, low-power consuming, and easy-to-install. On the other hand, the currently
available BLE beacons can only support an Received Signal Strength Indicator
(RSSI), which is the signal strength information. A localization method called
RSSI-ranging [2] using the RSSI has been widely studied. However, RSSI rapidly
fluctuates by location due to the multipath-fading effect, and it is difficult to es-
timate the accurate distance only from the RSSI. As a more accurate localization
method, DOA/DOD (Direction-of-Arrival/Departure) estimation method [3]-[4]
has been studied. In the methods [4] , the arrival / departure direction is esti-
mated from the correlation matrix of the array response using 90 degree and 180
degree hybrid circuits and an array antenna. However, when there is large errors
2 D. Kitamura N. Honma et al.
Fig. 1. RSSI-ranging using pattern diversity
in estimated DODs due to multipath-fading effect, the localization accuracy is
extremely lowered. The alternative wireless method is epitomized by the finger-
printing technique [5] . This method can identify the location accurately, but the
RSSI-distribution database needs to be created before the localization. Moreover,
the database is easily outdated and needs to be rebuilt if the arrangement of the
furniture in the environment is changed. For there reasons, the radio-wave-based
robust technique for improving localization accuracy is needed.
In this research, we propose a method to improve localization accuracy by
introducing RSSI-ranging with a pattern diversity and distance-based reliability
function technique. The experiment in the indoor environment was carried out
to verify the accuracy of the proposed method, and its performance compared
to the conventional methods are discussed.
2 RSSI-Ranging Method Using Pattern Diversity and
Distance-Based Reliability Function
In this section, we will show the method to improve the localization accuracy us-
ing RSSI-ranging with pattern diversity and distance-based reliability function.
RSSI-ranging is performed using received signals transmitted from multiple bea-
cons. However, the estimated distance for each beacon is not accurate due to the
multipath fading. The pattern diversity is introduced to improve the ranging
accuracy by alleviating the multipath effect. The reliability function is used to
take multiple ranging results into account when each result has a high ambiguity
in distance. The following discussions explain how these issues are managed in
this study.
2.1 RSSI-Ranging Method Using Pattern Diversity
In the proposed method, The RSSI-ranging is performed using the RSSI observed
at the terminal. In order to consider the ambiguity in the estimated distance,
a evaluation function based on a Gaussian function is used, and the sum of
the evaluation function corresponding to all beacons is used to enhance the
localization accuracy. A conceptual sketch of the RSSI-ranging using a pattern
Accuracy Improvement Method in RSSI-Ranging 3
diversity is shown in Fig. 1. In this study, the multibeam feed network developed
in our previous work [4] is used to obtain a high directive gain over the wide
angular range. A 180-degree hybrid is used for the pensile and V-shaped patterns.
A 90-degree hybrid is used for left and right directed patterns. The combiners are
connected to the antennas, and collect the signals from both hybrids. To maintain
the radiation pattern, the dummy loads are used at both sides of the circuit
because the unwanted reflection from the circuit ends will affect the radiation
patterns. As shown in Fig.1, the envelope directivity covers wide directions while
each pattern has a high gain as well as the narrow beam suitable for rejecting
the unwanted multipaths. Note that this circuit functions almost identically for
all frequencies transmitted by the beacon signal generators because the hybrid
bandwidth is much wider than the used frequency bandwidth ('3%). In the
proposed method, the beacon signals emitted from a 3-element array antenna
with 4 different patterns are received by a commercially available smartphone.
The RSSI-ranging is performed by choosing the most strongest RSSI among all
patterns. This idea contributes to avoiding the signal strength dips due to the
multipath fading, and the distance can be estimated stably.
2.2 Distance-Based Localization Using Reliability Function
Even though the RSSI-ranging accuracy is improved by using the pattern di-
versity technique, there still exists ambiguity in the estimated distance. The
RSSI-ranging method tends to incur a large error due to multipath-fading effect
especially when the distance between the beacon and terminal is large. To miti-
gate the effect of the fading on the localization, the beacon with higher RSSI is
more trusted than other beacons, and this idea realizes further improvement in
the localization accuracy. The second key idea is a weighting scheme, which is
introduced to consider the reliability of the estimated distances from all beacons.
An evaluation function based on a Gaussian function is introduced to consider
the ambiguities in the estimated distance. In this scheme, the higher reliability
value is given for the closer beacon, while the lower value is given for the distant
beacon. The distance-based reliability function W is defined as the reciprocal of
the power p of the estimated distance. p corresponds to the path-loss and this is
the simplest path-loss model commonly used in the various situations including
the indoor and outdoor environments [6],[7] . In the proposed method, p is ex-
perimentally determined in advance. The evaluation functions corresponding to
all beacons are weighted by the reliability coefficient and summed to calculate
the total evaluation function.
N
X
Franging = W (n)franging (n) (1)
n=1
Where, N is the number of Tx and franging is evaluation function of RSSI-
ranging. The summation operation is used to take all evaluation functions into
account. The multiplication is not used because the correct solution disappears
if one of the functions strays significantly. Finally, the receiver’s location is esti-
mated by searching the point, where the sum evaluation functions is maximum.
4 D. Kitamura N. Honma et al.
3 Experiment
Tx2 Tx3
3m
22.5 29 2 m 30 31 32 33 34 35 36
Obstacle 5m 5m
17.5 22 23 24 25 26 27 28
5m 5m
1.6 m
25 m
12.5 14 15 16 17 18 19 20 21
17 m
Obstacle 7.5 6 7 8 9 10 11 12 13
20 m 14 m Tx4
3m
Beacon 2.5 1 2 3 4 6m Obstacle 5
Tx1
Tx2 1 6 11 16 21 26 31 36
37 m
(a)Experimental environment (b)Measurement point and coordinates
Fig. 2. Experiment setup
Tx Rx
Fig. 3. Used transmitter and receiver
3.1 Experimental Conditions and Environment
In this experiment, we performed indoor localization experiment using the BLE
beacons and smartphone terminal, and evaluated the localization accuracy. To
verify the performance of the ranging scheme without the effect of the human
body, we conducted the experiment with holding the terminal above the head
of a person. The receiver’s height is about 2 m. The experiment environment
is detailed in Fig. 2. This experiment was conducted in a 25 m × 37 m indoor
environment. The measurements were performed at 36 points at 5 m interval in
both vertical and horizontal directions. The metallic obstacles are intentionally
located to consider the effect of scatterers whose heights are about 1.6 m. The
beacon transmitters were placed at the four corners of the measurement area.
The locations of the transmitters are Tx1 (0.57 m, 0.55 m, 2.91 m), Tx2 (0.58
m, 24.5 m, 1.65 m), Tx3 (36.5 m, 24.4 m, 1.65 m), Tx4 (36.4 m, 6.55 m, 1.65
m). Besides, All transmitters direct the center of the measurement area. Fig. 3
shows a photo of the beacon transmitter and receiver used in the experiment.
Accuracy Improvement Method in RSSI-Ranging 5
100
Max
Mean
75 only
only
CDF [%]
50
25
00 5 10 15 20
Estimation error [m]
Fig. 4. CDF of localization error with and without diversity effect
The receiver is commercially available smartphone without any alteration. As
a transmitter, we used a device, which has a 3-element patch array antenna
and transmits 4 beacon signals with 4 different patterns. 4-port feed network
is connected to the transmitter via SP4T switch and the signal is sequentially
switched all ports. The set of the beacon signal consists of 3 frequencies (adver-
tising channels), i.e. 2.402 GHz, 2.426 GHz, and 2.480 GHz, and is transmitted
by every 20 ms.
3.2 Result 1: Performance of Pattern Diversity
Fig. 4 shows the cumulative distribution function (CDF) of the localization error
with the diversity scheme. We also show the CDFs of the localization errors
when RSSI-ranging is performed using the average of all ports, only port h180−1 ,
and only port h90−1 instead of the diversity scheme. The average and median
localization errors of the mean-RSSI based ranging are 8.89 m and 7.99 m,
respectively. On the other hand, the average and median localization errors of
the diversity scheme are 7.77 m and 6.13 m, respectively. Therefore, it is found
that the localization accuracy can be improved by selecting the port with the
highest RSSI. Note that the diversity scheme described above is adopted in the
following results.
3.3 Result 2: Effect of Distance-Based Reliability Function on
Localization Accuracy
The localization using the evaluation functions discussed above was evaluated,
where the average of the Gaussian function is 0, and the variance is set to 100 m
in this evaluation. In the proposed method, the constant p must be determined in
advance of the localization. In this study, the two values, p = 0 and p = 0.8, were
tested, where p = 0 means all estimated distances are equally trusted because
weighting value is always 1 independently to the distance. Fig. 5 shows CDF of
localization errors when p = 0.8 and p = 0. When the value is set to p = 0, the
average and median errors are 7.77 m and 6.13 m, respectively. When the value
is set to p = 0.8, the average and median errors are improved to 6.70 m and 4.62
m, respectively.
6 D. Kitamura N. Honma et al.
100
With weight (p = 0.8)
75
CDF [%]
50
25 Without weight (p = 0)
0
0 2 4 6 8 10 12 14
Estimation error [m]
Fig. 5. CDFs of localization error with and without reliability weighting technique
4 Conclusion
This paper has studied the accuracy improvement method of the RSSI-ranging
based localization, where the pattern diversity and distance-based reliability
function are jointly used. The experiment in the indoor environment revealed
that the average and median errors are improved by 1.22 m and 1.86 m, respec-
tively, compared to that of the non-diversity scheme. Moreover, the distance-
based reliability function improves the average and median errors by 1.07 m
and 1.51 m, respectively. Currently, the combination of the proposed method
and DOD-based localization is experimentally studied for the further accuracy
improvement, and a part of this result will be presented in the conference.
References
1. M. L. Sichitiu and V. Ramadurai, “Localization of wireless sensor networks with a
mobile beacon,” 2004 IEEE International Conference on Mobile Ad-hoc and Sensor
Systems, pp. 174-183, Oct. 2004.
2. S. Maddio, A. Cidronali, and G. Manes, “RSSI/DoA based positioning systems
for wireless sensor network,” New Approach of Indoor and Outdoor Localization
Systems, InTech, pp. 139-162, 2012.
3. Z. Shan and T.-S. P. Yum, “Precise localization with smart antennas in ad-hoc net-
works,” in Proc. 2007 IEEE Global Telecommunications Conference (GLOBECOM
2007), pp. 1053-1057, Nov. 2007.
4. R. Tazawa, N. Honma, A. Miura, and H. Minamizawa, “RSSI-based localization
using wireless beacon with three-element array,” IEICE Trans. Commun., Vol. E101-
B No. 2, pp. 400-408, Feb. 2018.
5. K. Kaemarungsi, P. Krishnamurthy, “Modeling of indoor positioning systems based
on location fingerprinting,” The 23 annual joint conference of the IEEE computer
and communications societies, vol.2, pp. 1012-1022, Mar 2004.
6. K. Nishimori, R. Di Taranto, H. Yomo, P. Popovski, Y. Takatori, R. Prasad and S.
Kubota,“Spatial opportunity for cognitive radio systems with heterogeneous path
loss conditions,” IEEE Vehicular Technology Conference (VTC 2007), pp. 2631-
2635, Apr. 2007.
7. J. Medbo, and J. -E. Berg, “Simple and accurate path loss modeling at 5 GHz
in Indoor Environments with corridors,” IEEE Vehicular Technology Conference
(VTC 2000), pp. 30-36, Sep. 2000.