A Position Estimation System
Making Use of Signal Strengthof Wireless LAN in a Shipyard

Hajime Kimura,
Nariki Iwauchi,
Yuuichi Yoshida,

Kyushu University, JAPAN
Tsuneishi Shipbuilding Co., Ltd., JAPANKawasaki Heavy Industries, Ltd., JAPANICCAS 2019, 24-26 September 2019, Rotterdam, Netherlands

Background
It is required to obtain
Information of the position ofworkers or parts of products

to manage
production schedules,
stock of parts, orsafety of workers

We want to…
Record the position by touching
button of the mobile terminal tomanage stock of parts.

Get trajectory of workers toimprove production efficiency.

Background
The most standard method to obtain the position is GPS,
however it cannot be used in buildings with roofs.

GPSsatellite

Background

GPSsatellite

There are several special devices and services
to obtain the position in buildings with roofs, where GPS cannot be used.
Unfortunately, it requires large cost of installingthe special equipment, or large cost ofmaintenance.
Wireless
BeaconLAN

BeaconMotivations
Meanwhile, the infrastructure of the wireless LAN
has been in place in recent factories.
GPSsatellite

WirelessLAN

WirelessLANMotivationsMeanwhile, the infrastructure of the wireless LAN
has been in place in recent factories.
GPSsatellite

Can we make use of the wireless
LAN for the position estimation?
WirelessLANWirelessLAN

Existing Wireless LAN location detection methods(a) Triangulation:(b) Proximity：(c) Scene Analysis：

Existing Wireless LAN location detection methods(a) Triangulation:(b) Proximity：

Positions of the Basestations are known

Wireless base station
Distance calculated from radio intensity

Candidate of the position
Estimated position(c) Scene Analysis：

Existing Wireless LAN location detection methods(a) Triangulation:(b) Proximity：

Positions of the Basestations are known

Positions of the Basestations are known

Wireless base station
Distance calculated from radio intensity

Wireless base station
Area where base stations cover

Candidate of the position

Estimated area

Estimated position

Estimated position(c) Scene Analysis：

Existing Wireless LAN location detection methods(a) Triangulation:(b) Proximity：

Positions of the Basestations are known

Positions of the Basestations are known

Wireless base station
Distance calculated from radio intensity

Wireless base station
Area where base stations coverCandidate of the position

Estimated area

Estimated position

Estimated position(c) Scene Analysis：
Observing scenes at many positions,
Constructing each database or machine learning
Blue: Observed BSSID intensity andpositions in advanceGreen: BSSID intensity of a query andits estimated position

Information of positions of theBase stations are not used

Position Estimation Process in Scene Analysis(1) Information of Wi-Fi radio intensity is collected
at many positions in the target area.

Pre-process to generateradio intensity maps
(2) Radio intensity map for each BSSID (ID ofwireless LAN base station) is generated.

Data-matching(3) Query position estimation using the radio intensity map

Example 1: A Research Building in Kyushu University

Target area

Example 1: A Research Building in Kyushu University

Hall way
Width 3[m]Example 1: A Research Building in Kyushu University
Observed points on the floor map, andDelaunay triangulations
Average length of the Delaunay edges: 10.64[m]Length of the building: 137[m] width:30[m]Number of the observed points: 62
Average number of the BSSID: about 30 in one point,
Total 273 BSSID are observed.

Radio Intensity [dB]

Generating Radio Intensity Maps from Observed Data
Radio intensity maps are generated by Delaunay
triangulation and linear interpolation

Distance
Radio wave intensity [dB]
and distance can beapproximated linearly

RadioIntensity ofBSSID_x

Linear interpolation is practicalbecause the positions of antennas
are unknown in general.

Observed
point

Observed
point

Observed
point

Generated Wi-Fi Radio Intensity Maps (Total 273 maps）

Radio intensity map of BSSID15Radio intensity map of BSSID67

Query Position Estimation using the Radio Intensity Maps (1)Observed BSSID_x’s Radio Intensity
at the unknown query position

RadioIntensity ofBSSID_x

Observed
point

Adding all BSSID’s similarity values on the map

Observedpoint
The position where theradio intensity is the same
= estimated position

Observedpoint

Adding all BSSID’s estimatedposition on the map, then…

The position where theradio intensity is the same

Wireless BASEstation(the position isunknown)

Wireless BASEstation(the position isunknown)

Query Position Estimation using the Radio Intensity Maps (2)Maximum value of the similarity is 1
when the radio intensity is the same

1Similarity

P1

P00

Difference between query’s radio intensity
and radio intensity on the map of BSSID_x

Shape with negative value:
Similar to Edge Filter

The shape of the similarity function isobtained automatically using nonlinear
optimization using the value in the convex
hull of the position of the observation data

The shape of optimized
similarity function for thebuilding of Kyushu Univ.

Estimation Results （Example 1: Kyushu University 6F）
Estimated position
True position
The contour shows similarity,
error=1.264[m]error=0.63[m]For all measurement points inside the convex hull, if the point is excluded from the database and theposition is estimated from other measurement points, the average error from the true position is 2.46 [m]Example 2：Assembly Line for Small Blocks in a Shipyard

Observed points on the floor map, andDelaunay triangulations
Average length of the Delaunay edges: 26.9[m]Length of the building: 248[m] width:43[m]Optimized
shape of thesimilarity
function

Number of the observed points: 28
Average number of the BSSID: about 20 in one point,
Total 29 BSSID are observed.

Generated Wi-Fi Radio Intensity Maps (Total 29 maps）

Radio intensity map of BSSID4Radio intensity map of BSSID14

Estimation Results （Example 2: Assembly line in a shipyard）
Estimated position

True position

The contour shows similarity

Error=1.29[m]Error=7.86[m]For all measurement points inside the convex hull, if the point is excluded from the database and theposition is estimated from other measurement points, the average error from the true position is 12.3 [m]Example 3：Research Building 2F Hall in Kyushu Univ.

Example 3：Research Building 2F Hall in Kyushu Univ.

Observed points on the floor map, andDelaunay triangulations
Average length of the Delaunay edges: 4.15[m]Length of the building: 20[m] width:30[m]Number of the observed points: 50
Average number of the BSSID: about 30 in one point,
Total 97 BSSID are observed.

Generated Wi-Fi Radio Intensity Maps (Total 97 maps）

Radio intensity map of BSSID0

Radio intensity map of BSSID3

Radio intensity map of BSSID5

Optimized shape of the similarity function.
Notice that there is no negative part.

Estimation Results （Example 3: Kyushu University 2F Hall）
Estimated position
True position
The contour shows similarity,

Error= 2.82[m]Error= 2.50[m]Error= 4.49[m]For all measurement points inside the convex hull, if the point is excluded from the database and theposition is estimated from other measurement points, the average error from the true position is 4.18 [m]Discussion１）Estimation accuracy is largely depend on the number andarrangement of the Wi-Fi antennas.Target area should be surrounded by Wi-Fi ant.
・Research Building 6F Average length between observed points:
10[m]Average number of BSSIDs: about 30 in one point, Error= 2.5[m]・Research Building 2F Average length between observed points:
4.1[m]
Average number of BSSIDs: about 30 in one point, Error= 4.18[m]・Assembly Line Average length between observed points:
26.9[m]Average number of BSSIDs: about 20 in one point, Error= 12.3[m]There is no Wi-Fi antenna in the inside of the target area.

２）Estimation accuracy can be improved by optimization of theshape of the similarity function.
Ex.) Initial error 6.28[m] is improved to 4.18[m] in Research Building 2F

Conclusion• we investigate the possibility to make use of existing Wi-Fi infrastructure
to estimate positions of workers or parts of the products in buildings whereGPS cannot be used.
•In our experiments, the accuracy of the system is 2.46 [m] in some special
conditions without any specific devices.
The best case

• If you want to improve accuracy of the system, the best way is to put
additional Wi-Fi antennas in the inside of the target area.

Acknowledgements
We thank Oshima Shipbuilding Co., Ltd for theexperiments in the factory building.

Remarks
This technology is Patent pending by Kyushu University.
(Japanese Patent Application No. 2018-095290)

If you want to get the source code of the programs,
Please contract with Kyushu university to use thelicense.