RSS and sensor fusion algorithms for indoor location systems on
smartphones
RSS and sensor fusion algorithms for indoor location systems on smartphones
Laia Descamps-Vila, A. Perez-Navarro and Jordi Conesa (and Andrés Gómez)
1
1. Context
2. Indoor positioning
3. Wifi Fingerprinting
Index
RSS and sensor fusion algorithms for indoor location systems on smartphones
3. Wifi Fingerprinting
4. Sensor Fusion
5. Test
6. Conclusions and Future Work
2
RSS and sensor fusion algorithms for indoor location systems on smartphones
3
1. Context
• Location Based Systems
• Context Awarerecommendation systems
Context
RSS and sensor fusion algorithms for indoor location systems on smartphones
recommendation systems
4
Need location!
Three main ítems about location:
• Coverage
Location
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Precision
• Security
5
Three main ítems about location:
• Coverage
Location
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Precision
• Security
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GNSS are the mainlocation systems…
Coverage
RSS and sensor fusion algorithms for indoor location systems on smartphones
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GNSS are the mainlocation systems…
Coverage
RSS and sensor fusion algorithms for indoor location systems on smartphones
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… but GNSS only workoutdoor
What happens indoor?
Coverage
RSS and sensor fusion algorithms for indoor location systems on smartphones
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How can we get the position indoor?
Question
RSS and sensor fusion algorithms for indoor location systems on smartphones
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•We only want to use infraestructuresalready installed
• The pass from outdoor to indoor
Our restrictions
RSS and sensor fusion algorithms for indoor location systems on smartphones
• The pass from outdoor to indoorenvironment should have to betransparent to the user
• The positioning should have to beperformed with the smartphone
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•We only want to use infraestructures already installed
• The pass from outdoor to indoor environment should have to betransparent to the user
Our restrictions
RSS and sensor fusion algorithms for indoor location systems on smartphones
• The positioning should have to beperformed with the smartphone
• Without internet connection• End user application
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Movistar and Vodafone 3G and 2G coverage
Why without internet connection?
RSS and sensor fusion algorithms for indoor location systems on smartphones
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Source: www.sensorly.com
•Fast enough to favor a satisfactory user’s experience
• High precision
Why does “end user application means”?
RSS and sensor fusion algorithms for indoor location systems on smartphones
• High precision
• Robust
• Easy to use
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1. Context
2. Indoor positioning
3. Wifi Fingerprinting
Index
RSS and sensor fusion algorithms for indoor location systems on smartphones
3. Wifi Fingerprinting
4. Sensor Fusion
5. Test
6. Conclusions and Future Work
15
RSS and sensor fusion algorithms for indoor location systems on smartphones
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2. Indoor Positioning
• Markers
• Wireless systems
How to get indoor positioning?
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Wireless systems
• Inertial systems
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• Markers distributed within the building (like QR codes).
• Easy and cheap to install
Markers
RSS and sensor fusion algorithms for indoor location systems on smartphones
• User and environment dependent
• Is not a true positioning system
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• There are “beacons” distributed within the building (WIFI, bluetooth, RFID)
• The position is calculated by triangulation or any other positioning method
Wireless
RSS and sensor fusion algorithms for indoor location systems on smartphones
or any other positioning method
• Previous infraestructure should have to be installed
• Users need a device with a sensor for that kind of wave
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• The positioning is established by only using the internal sensors of the device.
• They are very cheap, because no
Inertial systems
RSS and sensor fusion algorithms for indoor location systems on smartphones
• They are very cheap, because no previous infraestructure is needed.
• Needs previous calibration.
• Nowadays accuracy is low.
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• Markers
• Wireless systems
Our approach
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Wireless systems
• Inertial systems
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• Markers
• Wireless systems
Our approach
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Wireless systems
• Inertial systems
22
• Markers
• Wireless systems
Our approach
WIFI
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Wireless systems
• Inertial systems
23
WIFI
• Markers
• Wireless systems
Our approach
WIFI
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Wireless systems
• Inertial systems
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WIFI
1. Context
2. Indoor positioning
3. Wifi Fingerprinting
Index
RSS and sensor fusion algorithms for indoor location systems on smartphones
3. Wifi Fingerprinting
4. Sensor Fusion
5. Test
6. Conclusions and Future Work
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RSS and sensor fusion algorithms for indoor location systems on smartphones
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3. Wifi Fingerprinting
• Calibration
RSS Fingerprinting
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Positioning
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Calibration: 1. Create a matrix of nodes
1 2
RSS and sensor fusion algorithms for indoor location systems on smartphones
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c
a
d
b
e
5
7
43
6
98
Calibration: 2. Detect Access Points from every node
1 2 Node b (Nb) Receives signalfrom Access Points (AP) 2, 4,
5, 6, 7 and 9
RSS and sensor fusion algorithms for indoor location systems on smartphones
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5
7
43
6
98
c
a
d
b
e
Calibration: 3 measure signal level from each AP at every node…
Node b
AP
Levelcalibration
2 4
RSS and sensor fusion algorithms for indoor location systems on smartphones
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2 4
4 9
5 10
6 1
7 5
9 3
Calibration: 3 measure signal level from each AP at every node… several times
Node b
AP
lcb,1 lcb,2 lcb,3 lcb,4 lcb,5
RSS and sensor fusion algorithms for indoor location systems on smartphones
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2 4 3.5 4.5 3.75 4.25
4 9 5 10 3 1
5 10 9.5 9.75 9.9 9.3
6 1 5 2 3 1
7 5 8 1 9 1
9 3 2.9 2.8 3.1 2.8
Calibration: 3 measure signal level from each AP at every node… several times
]],...,[],...,,...,[],...,,...,[[)( 111 nnni lclclclclclcnN =
Number of measures
Level of calibration
RSS and sensor fusion algorithms for indoor location systems on smartphones
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]],...,[],...,,...,[],...,,...,[[)(,,,,1,1,i kikijijiii
lclclclclclcnN =
Nodeidentifier
APidentifier
APmeasured
Calibration: 4 Calculate the mean and the standard deviation
Node b
AP
lcb,1 lcb,2 lcb,3 lcb,4 lcb,5 Mean StdDev.
2 4 3.5 4.5 3.75 4.25 4 0.4
RSS and sensor fusion algorithms for indoor location systems on smartphones
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2 4 3.5 4.5 3.75 4.25 4 0.4
4 9 5 10 3 1 5.6 3.9
5 10 9.5 9.75 9.9 9.3 9.7 0.3
6 1 5 2 3 1 2.4 1.7
7 5 8 1 9 1 4.8 3.8
9 3 2.9 2.8 3.1 2.8 2.2 0.1
Calibration: 4 Calculate the mean and the standard deviation
lcn
rji
cl∑
=, 2)(
1 nr cllc∑ −=σ
RSS and sensor fusion algorithms for indoor location systems on smartphones
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njir
ji
cl∑
= =1,
,
2,
1,, )(
1 jir
rjiji cllc
n∑ −
−=
=σ
Calibration: 5 Eliminate unstable valuesNode b
AP Meanlcb
StdDev.
2 4 0.4
4 5.6 3.9
A study with stable AP revealed thatfluctuations are always under 3
AP Meanlcb
StdDev.
2 4 0.4
Node b
RSS and sensor fusion algorithms for indoor location systems on smartphones
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4 5.6 3.9
5 9.7 0.3
6 2.4 1.7
7 4.8 3.8
9 2.2 0.1
We only keep the mean of several measuresAnd only from those stable AP’s
5 9.7 0.3
6 2.4 1.7
9 2.2 0.1
Calibration: 6 Keep only a limited number of AP’sNode b
Node b
AP Meanlcb
StdDev.
2 4 0.4
AP Meanlcb
StdDev.
2 4 0.4
RSS and sensor fusion algorithms for indoor location systems on smartphones
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We only keep a maximum number of AP’s: those more stableGOAL: To reduce the size of the calibration matrix
5 9.7 0.3
6 2.4 1.7
9 2.2 0.1
5 9.7 0.3
9 2.2 0.1
Calibration Matrix
],0(;],0(_ kjsiclmatrixCal ∈∈=
RSS and sensor fusion algorithms for indoor location systems on smartphones
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],0(;],0(_ , kjsiclmatrixCal ji ∈∈=
Calibration 7 Build the calibration matrix
Repeating the process calibration 1-6 for every node of thecalibration map, the calibration matrix is build
• Each node i has a maximum of k nodes associated.
RSS and sensor fusion algorithms for indoor location systems on smartphones
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• Each node i has a maximum of kmaxnodes associated.
• Every single node can detect different AP, so kmax is not the matrixdimension
Location
a b 543
1 2
P
RSS and sensor fusion algorithms for indoor location systems on smartphones
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c
d e
76
98
P
Location: 1 to take several measures
a b 543
1 2
RSS and sensor fusion algorithms for indoor location systems on smartphones
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c
d e
76
98
P
Location: to do as in calibration steps 3 to 6
Point P
AP
Mean StdDev.
4 5.1 0.5
RSS and sensor fusion algorithms for indoor location systems on smartphones
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4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
Location: to do as in calibration steps 3 to 6
]],...,[],...,,...,[],...,,...,[[)( 111 nnn lplplplplplpnP =
Number of measures
Level ofposition
RSS and sensor fusion algorithms for indoor location systems on smartphones
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]],...,[],...,,...,[],...,,...,[[)(,,,,1,1, mimijijiii
lplplplplplpnP =
Nodeidentifier
APidentifier
APmeasured(≠ k)
Location: 7 to calculate the “euclidean distance” to calibration nodes
We only calculate the distanceusing the same AP’s
AP
Mean lc b
StdDev.
2 4 0.4
5 9.7 0.3
9 2.2 0.1
Node b
Point P
RSS and sensor fusion algorithms for indoor location systems on smartphones
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AP
Mean StdDev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
9 2.2 0.1
AP
Mean lc b
StdDev.
4 4 0.4
7 9.7 0.3
9 2.2 0.1
Node cAP
Mean lc b
StdDev.
4 4 0.4
6 9.7 0.3
9 2.2 0.1
Node e
Location: 7 to calculate the “euclidean distance” to calibration nodes
Point P
AP
Mean lc b
StdDev.
2 4 0.4
5 9.7 0.3
9 2.2 0.1
Node b
2)2,24,2( −We only calculate the distanceusing the same AP’s
RSS and sensor fusion algorithms for indoor location systems on smartphones
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AP
Mean StdDev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
9 2.2 0.1
AP
Mean lc b
StdDev.
4 4 0.4
7 9.7 0.3
9 2.2 0.1
Node cAP
Mean lc b
StdDev.
4 4 0.4
6 9.7 0.3
9 2.2 0.1
Node e
22 )2,24,2()41,5( −+−222 )2.24.2()7.91.3()41.5( −+−+−
Location: 7 to calculate the “euclidean distance” to calibration nodes
Number of coincidents AP
RSS and sensor fusion algorithms for indoor location systems on smartphones
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2
1, )()( j
r
jjiii plcllNPl ∑ −==−
=
Location: 8 to divide by the number of coincidentsAP’s
Point P
AP
Mean lc b
StdDev.
2 4 0.4
5 9.7 0.3
9 2.2 0.1
Node b
1
)2.24.2( 2−
RSS and sensor fusion algorithms for indoor location systems on smartphones
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AP
Mean StdDev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
9 2.2 0.1
AP
Mean lc b
StdDev.
4 4 0.4
7 9.7 0.3
9 2.2 0.1
Node cAP
Mean lc b
StdDev.
4 4 0.4
6 9.7 0.3
9 2.2 0.1
Node e
2
)2.24.2()41.5( 22 −+−
3
)2.24.2()7.91.3()41.5( 222 −+−+−
Location: 9 to calculate the distance to nodes
Point P
1
)2.24.2( 2−Distance P-b=0.2 a.u.
RSS and sensor fusion algorithms for indoor location systems on smartphones
47
AP
Mean StdDev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
2
)2.24.2()41.5( 22 −+−
3
)2.24.2()7.91.3()41.5( 222 −+−+−
Distance P-e=0.6 Distance P-c=2.2 a.u.
Location: 9 to calculate the position
Point P
1
)2.24.2( 2−Distance P-b=0.2 a.u.
RSS and sensor fusion algorithms for indoor location systems on smartphones
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AP
Mean StdDev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
2
)2.24.2()41.5( 22 −+−
3
)2.24.2()7.91.3()41.5( 222 −+−+−
Distance P-e=0.6 Distance P-c=2.2 a.u.
2.21
6.01
2.01
2.26.02.0;
2.21
6.01
2.01
2.26.02.0;
2.21
6.01
2.01
2.26.02.0
++
++=
++
++=
++
++=
cebcebceb zzz
z
yyy
y
xxx
x
Location: 9 to calculate the position
∑∑
∑∑
∑∑
===
===q
iq
qi
q
qi
q l
zz
l
yy
l
xx
11
;1
1;
11
RSS and sensor fusion algorithms for indoor location systems on smartphones
49
∑∑
∑∑
∑∑
=
=
=
=
=
=
i iq
i i
i iq
i i
i iq
i i
ll
ll
ll
1
1
1
1
1
1
111
1. Context
2. Indoor positioning
3. Wifi Fingerprinting
Index
RSS and sensor fusion algorithms for indoor location systems on smartphones
3. Wifi Fingerprinting
4. Sensor Fusion
5. Test
6. Conclusions and Future Work
50
RSS and sensor fusion algorithms for indoor location systems on smartphones
51
4. Sensor Fusion
•Accelerometer
Sensor Fusion
RSS and sensor fusion algorithms for indoor location systems on smartphones
•Magnetometer
52
Accelerometer: acceleration
Axis
∑−=q j
iii
Fga
RSS and sensor fusion algorithms for indoor location systems on smartphones
53
∑=
−=j
ii mga
1
Accelerometer: acceleration
∑−=q j
iii
Fga
Gravity Force
Acceleration
RSS and sensor fusion algorithms for indoor location systems on smartphones
54
∑=
−=j
ii mga
1
Axis Forceidentifier
Mass of thedevice
Acceleration
Accelerometer: linear acceleration
i
q ji
ii gF
ga −−= ∑'
RSS and sensor fusion algorithms for indoor location systems on smartphones
55
ij
ii gm
ga −−= ∑=1
'
Accelerometer: position calculation
r0 r1 r
201 1'·
2
1tarr ∆+= rrr
212 2'·
2
1tarr ∆+= rrr
RSS and sensor fusion algorithms for indoor location systems on smartphones
56
O
r1 r2
r0 can be obtained from the GPS or from a calibration node.
Accelerometer: position calculation
)·'1
( 21 kk
n
k tarr ∆+=∑ −rrr
RSS and sensor fusion algorithms for indoor location systems on smartphones
57
)·'2
(1
1 kkk
k tarr ∆+=∑=
−
Accelerometer: axesy
RSS and sensor fusion algorithms for indoor location systems on smartphones
58
x
z
Accelerometer: axesy
RSS and sensor fusion algorithms for indoor location systems on smartphones
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x
z
True coordinate system depends onthe orientation or the smartphone
Accelerometer: axes transformationy
North
East
y’
RSS and sensor fusion algorithms for indoor location systems on smartphones
60
x
z
Altitude
z’
x’
Accelerometer + Magnetometer
Combination of both sensors allows to knoworientation of the smartphone
RSS and sensor fusion algorithms for indoor location systems on smartphones
61
Accelerometer: orientation matrix
y North
East
x’
y’
0º
y
RSS and sensor fusion algorithms for indoor location systems on smartphones
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Altitude
z’
x’y90º
y
180º
y
270º
1. Context
2. Indoor positioning
3. Wifi Fingerprinting
Index
RSS and sensor fusion algorithms for indoor location systems on smartphones
3. Wifi Fingerprinting
4. Sensor Fusion
5. Test
6. Conclusions and Future Work
63
RSS and sensor fusion algorithms for indoor location systems on smartphones
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5. Test
• RSS
Test
• Galaxy Nexus III• Android 4.3• Dual Core 1.2 GHz• 1 Gb RAM
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Sensor Fusion
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• 40 nodes
• 120 m2
• kmax=15
RSS Test: Building A (Flat)
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Threshold=3σ=6.18
• 100 tests
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• 40 nodes
• 120 m2
• kmax=15
RSS Test: Building A (Flat)
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Threshold=3 σ =6.18
• 100 tests
Maximum precision: 1,5 m
67
• 40 nodes
• 1,600 m2
• kmax=15
RSS Test: Building A (Flat)
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Threshold=3 σ =6.18
• 100 tests
Maximum precision: 1,5 m
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• 40 nodes
• 1,600 m2
• kmax=15
RSS Test: Building A (Flat)
RSS and sensor fusion algorithms for indoor location systems on smartphones
• Threshold=3 σ =6.18
• 100 tests
Maximum precision: 5 m
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• 2000 measures to study reliability
• 1,600 m2
Sensor Fusion Test
RSS and sensor fusion algorithms for indoor location systems on smartphones
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• 2000 measures to study reliability
• 1,600 m2
Sensor Fusion Test
RSS and sensor fusion algorithms for indoor location systems on smartphones
Error is higher than 40% in only 10 m!!!
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•High dependence on frequency of sample
• Constant bias error of the accelerometer: it increases
Sensor Fusion Test: Problems
RSS and sensor fusion algorithms for indoor location systems on smartphones
accelerometer: it increases even in static position
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1. Context
2. Indoor positioning
3. Wifi Fingerprinting
Index
RSS and sensor fusion algorithms for indoor location systems on smartphones
3. Wifi Fingerprinting
4. Sensor Fusion
5. Test
6. Conclusions and Future Work
73
RSS and sensor fusion algorithms for indoor location systems on smartphones
74
5. Conclusions an d Future Work
Conclusions
•An RSS and a sensor fusion technique have been implemented in a prototype
• RSS is able to give between 1.5 and 5 meters of accuracy, but is highly dependant on the environment
RSS and sensor fusion algorithms for indoor location systems on smartphones
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highly dependant on the environment
• Sensor fusion has low accuracy and depends on environment and has a bias error.
• All the techniques proposed work entirely within the smartphone.
• All the techniques proposed have a response time less than 5 seconds.
Future Work
• To improve reliability of AP’s.
• To study how volume of people affects precision
• To improve z axis accuracy.
RSS and sensor fusion algorithms for indoor location systems on smartphones
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• To improve z axis accuracy.
• To study where is the origin of the error.
• To study how to avoid the building dependence on the error.
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