International Conference on Computer Applications in Shipbuilding 2019, 24-26 September 2019, the Netherlands

POSITION ESTIMATION SYSTEM MAKING USE OF SIGNAL STRENGTH OFWIRELESS LAN IN A SHIPYARD
H Kimura, Kyushu University, Japan
N Iwauchi, Tsuneishi Shipbuilding Co., Ltd., JapanY Yoshida, Kawasaki Heavy Industries, Ltd., JapanSUMMARY
In shipyards, it is required to obtain the position of people and goods in order to manage people, objects, improve work
placement, and secure workers' safety. Although the most popular location estimation is GPS, it cannot be used for position
detection in a building with a roof. Meanwhile, the infrastructure of the wireless LAN has been in place in recent factoriesof shipyards, and if these can be used for position detection, the installation cost of dedicated facilities for position
detection can be reduced. In this research, we investigate a location estimation method using radio field intensity ofunspecified number of wireless LANs without specifying the access point installation site of wireless LAN, and verify the
position estimation accuracy inside the building of the shipyard.
1.

INTRODUCTION

In shipyards, it is required to obtain the position of people
and goods in order to manage people, objects, improve
work placement, and secure workers' safety. Although themost popular location estimation is GPS, it cannot be used
for position detection in a building with a roof. Instead,
there exists devices and services that use radio waves orultrasonic waves to specify the location in a building [1].
They are used by installing special devices such as
dedicated beacons, antennas, and wireless devices.
However, using such special equipment and services in a
shipyard is unfortunately not cost effective. Meanwhile,the infrastructure of the wireless LAN has been in place inrecent factories of shipyards, and if these can be used for
position detection, the installation cost of dedicatedfacilities for position detection can be reduced. In thisresearch, we investigate a location estimation method
using radio field intensity of unspecified number ofwireless LANs without specifying the access point
installation site of wireless LAN, and verify the position
estimation accuracy inside the building of the shipyard.
2.
LOCATION ESTIMATIONMAKING USE OF WIRELESS-LAN

METHODS

Wireless base station
Distance calculated from radio
intensity

Candidate of the position
Estimated position

Fig.1 Triangulation(b) Proximity: A method to identify which wireless LAN
base is close to the estimation target by the reception
strength of the estimation target. The location informationof the wireless LAN base station and the range coveredby each base station must be grasped in advance, and theestimation result is relatively low because it is the entire
range covered by the wireless base station. It cannot be
used in this research because it requires location
information of the base station as in (a).

Existing wireless-LAN location detection methods areroughly divided into the following three types:(a) Triangulation: It is a method to perform position
estimation using relative distance from the base station
whose position is known (Fig.1). The distance iscalculated using the radio intensity of the base station (WiFi router) observed at the terminal to be estimated.
However, radio waves are often not attenuated according
to the distance characteristics due to the effects ofreflected waves and scattered waves from obstacles andwalls, so it is difficult to apply them indoors with manywalls and fixtures. This method cannot be used because
we do not use the location information of the wireless basestation in this research.

Wireless base station
Area where base stations cover

Estimated area
Estimated position

Fig.2 Proximity© 2019: The Royal Institution of Naval Architects

International Conference on Computer Applications in Shipbuilding 2019, 24-26 September 2019, the Netherlands(c) Scene Analysis: Scene analysis is a method of
observing scenes at a plurality of positions in the target
area, constructing each database and its observed position
information into a database or machine learning in
advance, and using it for position estimation. At the timeof estimation, the scene observed from the terminal to be
estimated is compared with the database prepared inadvance, and the position is estimated based on which
position in the database is similar to the observed scene.
In this method, the influence of reflected waves andscattered waves is taken into consideration, andbynarrowing the distance between points to be observed inadvance, it is possible to improve the position estimation
accuracy instead of increasing the amount of data. Thismethod is adopted in this research.

Blue: Observed BSSID intensity and positions in advanceGreen: BSSID intensity of a query and its estimated position

Fig.3 Scene Analysis

3.
A NEW LOCATION
ESTIMATIONALGORITHM IN SCENE ANALYSIS
3.1
FLOW OF THE POSITION ESTIMATION
An overview of the position detection procedure of thismethod is shown in Fig.4.

(2) Create radio intensity map for each BSSID (ID of
wireless LAN base station)(3) Query position estimation using the radio intensity
map
Fig.4 Flow of the proposed position estimation system(1) Collection of BSSID radio intensity information in
the measurement area: Wireless LAN radio intensity
information is collected in the area where position
detection is performed. Although an image data of the area
is required, a simple floor guide may be used. Using atablet PC with a wireless LAN communication function,measure BSSID, which is the identification number of thewireless LAN base station, and its radio wave strength
multiple times (50 times in this research, about 3 minutes)
for one point and take a median. They are combined withmeasurement position information on the drawing toconstruct a database. Wireless LAN BSSID information
and radio intensity signal is obtained by “iwlist” commandon Linux OS and Python script. Because a lot of data thatis not required only with the result of the “iwlist”
command is included, we made a program that records
only the required ones. The position of each measurement
point is input to the system using the image data of the area
where position detection is to be performed.
(2) Create radio intensity map for each BSSID (ID of
wireless LAN base station): A radio field strength mapof the target area is created using the measured radio
intensity and the position information on the image data.
Although the radio wave intensity is originally given only
by "points" on the actually measured drawing, the radio
wave estimated intensity at places not observed on thedrawing is linearly interpolated from the intensities of theobservation points around them. In the convex hull area ofthe measurement points on the drawing, Delaunay
triangulation with the measurement points as the seeds is
performed, and the radio wave intensity is linearlyinterpolated to create the radio intensity map of theposition detection area.

Radio Intensity[dBm]

As a previous study of position estimation by Scene
Analysis, Fujita et al. [3] assumed wireless LAN received
radio wave intensity as a normal distribution, and mixed
radio wave received intensity model with Gaussian
mixture model using linear combination of
normaldistribution called normal mixture. And the systemestimates the position by maximum likelihood estimation
after making it. However, in this method, the couplingweights are manually adjusted during mixed normal
distribution model generation, and the effectiveness isquestioned, such as creating a model using far more basis
functions than observed data points for model generation.
In this study, we propose a new method of performing
position estimation using a method similar to image
filtering, which performs function approximation withminimum necessary resources for previously observed
data points.

(1) Collection of BSSID radio intensity informationin the measurement area

Distance[m]Fig.5 Relation between distance and the signal intensity.

© 2019: The Royal Institution of Naval Architects

International Conference on Computer Applications in Shipbuilding 2019, 24-26 September 2019, the Netherlands

Fig.5 shows an example of measurement results of therelation between distance from the LAN base station andreceived signal intensity. There is a lot of noise, but theradio intensity [dBm] and the distance are almost
proportional in the range shown in the graph. For thisreason, linear interpolation for the estimation of radio
intensity is reasonable in the area between themeasurement points as shown in Fig.6.

position detection area is a query position, and its
estimated radio intensity is given by interpolating thevertex height of the triangle. Fig.7 shows an example ofDelaunay triangulation of the target area with the observed
points as the seed points. Fig.8 shows an example of thecorrespondence between the map of the target area and thesignal strength map of a certain BSSID.

Fig.8 An example of a certain BSSID’s intensity map.

Fig.6 Interpolated signal intensity from the observed data.

(3) Query position estimation using the radio intensity
map: Acquire BSSID and radio wave intensity observedby the terminal whose position you want to estimate, anduse it as a query. The purpose of this system is to estimate
the position of the query by comparing the radio intensityof the query with the BSSID radio intensity maps createdin advance. The position estimation is realized by
calculating a "similarity" of the query's radio signal for all
meshes in the radio intensity map. The estimated query's
location is given by a position where the similarity isgetting largest value in the map. The simplest way to give"similarity" is calculating reciprocal of absolute value ofthe difference between the query's radio intensity and theintensity on the map for each BSSID. But it causes that thepeak area of the similarity becomes flat as shown in Fig.10.
The flat similarity peak is interfering with the estimation
accuracy of the query position.

Fig.9 An example of simple similarity map by using
reciprocal of absolute value of the difference between thequery's intensity and the intensity on the map.

Fig.7 Delaunay triangulation with the observed points.
The vertical axis (blue) in Fig.6 is the radio wave intensityof a certain BSSID, the plane represented by the red andgreen axes is the position detection area, and each vertexof the triangle in the figure is the position where theobservation was performed and its height represents
measured radio intensity. A point inside the triangle in the© 2019: The Royal Institution of Naval Architects

Therefore, we propose a new position estimation methodfrom the radio intensity maps. Instead of simple
calculating reciprocal of absolute value of the difference
between the query and the intensity map, we use a newsimilarity function as shown in Fig.10. It is a piecewise
linear function of the difference of the intensity between
the query and the intensity map. Maximum value of thefunction is fixed to 1 where the difference is zero, and thevalue decreases as the absolute value of the difference
increases. Notice that the end of the function has anegative value (Fig.10). This shape has the same effect asInternational Conference on Computer Applications in Shipbuilding 2019, 24-26 September 2019, the Netherlands

an edge filter in image processing. The similarity value isgiven by the following equations:

University. We use Raspberry Pi 3 Model B+ with
Raspbian OS as the wireless LAN device for observing
BSSID radio intensity.

(1)

𝐷 = 𝑥 (𝑄) − 𝑥 (𝑀)𝑃 𝐷 + 1 , where 𝑃 < 𝐷 ≤ 0
𝑆 = 𝑃 𝐷 + 1 , where 0 < 𝐷 < 𝑃 (2)0, otherwise
( )

S=∑

(3)

10

Where 𝐷 is the difference between the radio intensity ofthe query and each BSSID's intensity on the map, 𝑆 is thesimilarity of each BSSID, 𝑛 is the number of BSSIDs,
𝑥 (𝑀) is the radio intensity value at a certain location in
the intensity map, and 𝑥 (𝑄) is the query's radio intensity
observed at the place. The index i represents a certain
BSSID. The parameters 𝑃 , 𝑃 , 𝑃 and 𝑃 shown in theequation (2) are characterizing the shape of the similarity
function 𝑆 , and are adjusted for the intensity map with 𝑃
shown in the equation (3). All the BSSID share the same
similarity function. The parameter 𝑃 reflects themagnitude of the similarity function in proportion to each
BSSID's radio intensity at the query location.
1

𝑆
0

𝑃

𝑃
0

0

0

Fig.11 The floor map, observed points and Delaunay
triangulation in the experiment at Kyushu University.
The target area is about 137[m] length and 30[m] width,
the number of observed points is 62 (including query
points), the average length of the edges in the Delaunay
triangulation is 10.64[m] as shown in Fig.11. The numberof observed BSSID is varied depending on the location,with a minimum of 20 and a maximum of 54. The averageof the observed BSSID is about 30. The observed radio
intensity is also varied, with a minimum -90[dBm] and amaximum of -31[dBm]. Fig.12 shows the optimized
similarity function 𝑆 , where 𝑃 = −7.00, 𝑃 =0.172, 𝑃 = 10.72 and 𝑃 = −0.030. The parameter 𝑃
is also optimized as 𝑃 = 27.89 in this experiment. The
average of the estimation errors over the inside of theconvex hull of observation points is 2.456[m].

𝐷0

Fig.10 Similarity function 𝑆 for a BSSID indexed by i.
The parameters of the similarity function areautomatically adjusted by self-tuning, that is, each point in
the convex hull region of the observation point group isselected one by one and regarded as a temporary query
point, and the difference between the estimated positions
and true positions of the temporary query points are used
for the adjustment of the similarity function parameters.
Our system is making use of Down-hill simplex method
for the parameter adjustment. Note that the maximum
value of the similarity S depends on the query signal
intensity and the number of observed BSSID and so on. In
our system, the value of the similarity S is normalized withthe maximum value being 1 for convenience.

Fig.12 Optimized Similarity function 𝑆 in the experiment.

4.

EXPERIMENTS

4.1

A RESEARCH BUILDING IN KYUSHU UNIV.

Fig.13 An example of the position estimation results. The
blue cross in the figure represents the actual query position,and the red cross represents the estimated position. The
estimation error is 1.22[m].

In order to examine the practicality of the proposed
position detection system, an experiment was conductedon the 6th floor of Building W2 at Ito Campus, Kyushu

The data collection positions and the query positions are
only the aisle in the building, and some position estimation
accuracy can be obtained while reflection and attenuation© 2019: The Royal Institution of Naval Architects

International Conference on Computer Applications in Shipbuilding 2019, 24-26 September 2019, the Netherlandsof radio waves by the wall are expected. Fig.13 shows anexample of the position estimation. In the estimation
calculation, the data of the query position is removed fromthe database of the system. The estimation result is
displayed as a contour of the similarity map, and theposition that has the maximum similarity is the estimatedposition.
4.2
INSIDE A SMALLFACTORY IN A SHIPYARD

BLOCK

Fig.16 shows an example of the position estimation.

ASSEBLY

Our system is applied to inside of a factory building ofassembly plant for small blocks equipped with wireless
LAN networks. there are a large number of small steel
plates and small blocks being transported by cranes in thefactory building, and electric welding work etc. are
performed. The target area of the experiment is about 43[m] x 248 [m], and the wireless standard is 802.11g. The
number of observed points is 28 (including query points),
the average length of the edges in the Delaunay
triangulation is 26.86[m] as shown in Fig.14. The numberof observed BSSID is varied depending on the location,with a minimum of 17 and a maximum of 23. The averageof the observed BSSID is about 20. The observed radio
intensity is also varied, with a minimum -91[dBm] and amaximum of -58[dBm]. Fig.15 shows the optimized
similarity function 𝑆 , where 𝑃 = −11.23, 𝑃 =0.0.846, 𝑃 = 6.851 and 𝑃 = −0.395. The parameter 𝑃
is also optimized as 𝑃 = 20.65 in this experiment. The
average of the estimation errors over the inside of theconvex hull of observation points is 12.3[m].

Fig.14 The floor map, observed points and Delaunay
triangulation in the factory building.

Fig.15 Optimized Similarity function 𝑆 in the experiment.

© 2019: The Royal Institution of Naval Architects

Fig.16 An example of the position estimation results. The
blue cross in the figure represents the actual query position,and the red cross represents the estimated position. The
estimation error is 7.90[m].

International Conference on Computer Applications in Shipbuilding 2019, 24-26 September 2019, the Netherlands

5.

DISCUSSION

The estimation accuracy largely depends on the number ofwireless LAN BSSIDs that can be received and the radio
intensity. When the number of BSSIDs is small or the
reception strength is weak, the estimation accuracy isdegraded. In the experiment at the research building, 30
BSSIDs were detected per one measurement point, and theradio wave reception intensity was often 50-60 [dB], so
the estimation accuracy was 2.456 [m]. On the other hand,in the factory, 20 BSSIDs were detected at one
measurement point, but the radio wave reception strength
was often as weak as 70-80 [dB], and the estimation
accuracy was greatly reduced as 12.3 [m]. Unlike theexperiment at the research building, there are also cases
where a large steel plate is being carried immediately
beside during radio wave measurement, which may beconsidered to be affected as noise.
The parameters 𝑃 , 𝑃 , 𝑃 , 𝑃 and 𝑃 for the similarity
function given in the equation (2) and (3) should be
optimized as shown in Fig.12 and Fig.15. The best valuesof parameters also depend on the condition of the target.
The proposed system can adjust the parameters
automatically.
The number of measurement points and the distanceinterval must also be set appropriately in accordance with
the number and intensity of BSSIDs that can be received.
The measurement point must be selected so that the peakof the received radio wave intensity does not come inside
the Delaunay triangle. Therefore, at the time of
measurement, pay attention to the location of the antennaof the wireless LAN device installed in the building.
In this experiment, the wireless LAN device used for datacollection of wireless LAN radio wave intensity was thesame as the device used for radio wave observation of thequery. It is necessary to investigate how the position
estimation accuracy is affected when these devices differ.
6.

7.

ACKNOWLEDGEMENTS

The authors thank Oshima Shipbuilding Co., Ltd for theexperiments in the factory building.
8.

REFERENCES

1.

Hitachi Systems, Ltd. ‘Wi-Fi real-time location
information
service’,
http://www.hitachisystems.com/solution/s002/musen_lan/wifi_location/.

2.

WLAN_ASSOCIATION_ATTRIBUTES
structurehttps://msdn.microsoft.com/enus/library/windows/desktop/ms706828(v=vs.85)
.aspx

3.

Fujita, S. Kaji, K. and Kawaguchi, N. 'A Wireless
LAN Location Estimation Method Based on
Gaussian Mixture Model', Journal of Information
Processing Society of Japan Vol. 52 No. 3 1069-1081 (Mar. 2011 in Japanese).

9.

AUTHORS BIOGRAPHY

Hajime Kimura holds the current position of Professor at
the Department of Maritime Engineering, Kyushu
University, Japan. His previous experience includes
optimization in shipbuilding.
Nariki Iwauchi belongs to Tsuneishi Shipbuilding Co.,
Ltd., Japan.
Yuuichi Yoshida belongs to Kawasaki Heavy Industries,
Ltd., Japan.

CONCLUSIONSIn this paper, we investigate the location detection of thewireless terminal indoors in the factory where GPS cannot
be used by using the existing wireless LAN infrastructure
as it is without new capital investment. Although it wasdifficult to detect the position with high accuracy, it
showed that it could be detected with an error of about 12[m] in a factory, 2.45[m] in a research building equippedwith wireless LAN networks, and showed the possibilityof grasping the position of people and things in real time.
The proposed system needs parameter adjustment for eachtarget, but they are performed automatically.
This technology is patent pending by Kyushu University
as Japanese patent application No.2017-96845.

© 2019: The Royal Institution of Naval Architects