RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones

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
4. Sensor Fusion
5. Test
6. Conclusions and Future Work
2

3
1. Context

• Location Based Systems
• Context Aware
recommendation systems
Context
recommendation systems
4
Need location!

Three main ítems about location:
• Coverage
Location
• Precision
• Security
5

Three main ítems about location:
• Coverage
Location
• Precision
• Security
6

GNSS are the main
location systems…
Coverage
7

GNSS are the main
location systems…
Coverage
8
… but GNSS only work
outdoor

What happens indoor?
Coverage
9

How can we get the position indoor?
Question
10

•We only want to use infraestructures
already installed
• The pass from outdoor to indoor
Our restrictions
• The pass from outdoor to indoor
environment should have to be
transparent to the user
• The positioning should have to be
performed with the smartphone
11

•We only want to use infraestructures already installed
• The pass from outdoor to indoor environment should have to be
transparent to the user
Our restrictions
• The positioning should have to be
performed with the smartphone
• Without internet connection
• End user application
12

Movistar and Vodafone 3G and 2G coverage
Why without internet connection?
13
Source: www.sensorly.com

•Fast enough to favor a satisfactory
user’s experience
• High precision
Why does “end user application means”?
• High precision
• Robust
• Easy to use
14

1. Context
Index
4. Sensor Fusion
5. Test
15

16
2. Indoor Positioning

• Markers
• Wireless systems
How to get indoor positioning?
• Inertial systems
17

• Markers distributed within the
building (like QR codes).
• Easy and cheap to install
Markers
• User and environment dependent
• Is not a true positioning system
18

• There are “beacons” distributed within
the building (WIFI, bluetooth, RFID)
• The position is calculated by triangulation
or any other positioning method
Wireless
or any other positioning method
• Previous infraestructure should have to
be installed
• Users need a device with a sensor for
that kind of wave
19

• The positioning is established by only
using the internal sensors of the device.
• They are very cheap, because no
Inertial systems
• They are very cheap, because no
previous infraestructure is needed.
• Needs previous calibration.
• Nowadays accuracy is low.
20

• Markers
Our approach
21

• Markers
Our approach
22

• Markers
Our approach
WIFI
23
WIFI

• Markers
Our approach
WIFI
24
WIFI

1. Context
Index
4. Sensor Fusion
5. Test
25

26

• Calibration
RSS Fingerprinting
• Positioning
27

Calibration: 1. Create a matrix of nodes
1 2
28
c
a
d
b
e
5
7
43
6
9
8

Calibration: 2. Detect Access Points from every node
1 2 Node b (Nb) Receives signal
from Access Points (AP) 2, 4,
5, 6, 7 and 9
29
5
7
43
6
9
8
c
a
d
b
e

Calibration: 3 measure signal level from each AP at
every node…
Node b
A
P
Level
calibration
2 4
30
2 4
4 9
5 10
6 1
7 5
9 3

every node… several times
Node b
A
P
lcb,1 lcb,2 lcb,3 lcb,4 lcb,5
31
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

every node… several times
]],...,[],...,,...,[],...,,...,[[)( 111 nnn
i lclclclclclcnN =
Number of
measures
Level of
calibration
32
]],...,[],...,,...,[],...,,...,[[)( ,,,,1,1,i kikijijiii
lclclclclclcnN =
Node
identifier
AP
identifier
AP
measured

Calibration: 4 Calculate the mean and the standard
deviation
Node b
A
P
lcb,1 lcb,2 lcb,3 lcb,4 lcb,5 Mean Std
Dev.
2 4 3.5 4.5 3.75 4.25
4 0.4
33
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
lc
n
r
ji
cl
∑
=
, 2
)(
1 n
r
cllc∑ −=σ
34
nji
r
ji
cl
∑
= =1
,
,
2
,
1
,, )(
1
ji
r
r
jiji cllc
n
∑ −
−
=
=
σ

Calibration: 5 Eliminate unstable values
Node b
AP Mean
lcb
Std
Dev.
2 4 0.4
4 5.6 3.9
A study with stable AP
revealed that
fluctuations are always
under 3
AP Mean
lcb
Std
Dev.
2 4 0.4
Node b
35
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 measures
And 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’s
Node b
Node b
AP Mean
lcb
Std
Dev.
2 4 0.4
AP Mean
lcb
Std
Dev.
2 4 0.4
36
We only keep a maximum number of AP’s: those more stable
GOAL: 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 ∈∈=
37
],0(;],0(_ , kjsiclmatrixCal ji ∈∈=

Calibration 7 Build the calibration matrix
Repeating the process calibration 1-6 for every node of the
calibration map, the calibration matrix is build
• Each node i has a maximum of k nodes associated.
38
• Each node i has a maximum of kmax nodes associated.
• Every single node can detect different AP, so kmax is not the matrix
dimension

Location
a b 543
1 2
P
39
c
d e
76
9
8
P

Location: 1 to take several measures
a b 543
1 2
40
c
d e
76
9
8
P

Location: to do as in calibration steps 3 to 6
Point P
A
P
Mean Std
Dev.
4 5.1 0.5
41
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 of
position
42
]],...,[],...,,...,[],...,,...,[[)( ,,,,1,1, mimijijiii
lplplplplplpnP =
Node
identifier
AP
identifier
AP
measured
(≠ k)

Location: 7 to calculate the “euclidean distance” to
calibration nodes
We only calculate the distance
using the same AP’s
A
P
Mea
n lcb
Std
Dev.
2 4 0.4
5 9.7 0.3
9 2.2 0.1
Node b
Point P
43
A
P
Mean Std
Dev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
9 2.2 0.1
A
P
Mea
n lcb
Std
Dev.
4 4 0.4
7 9.7 0.3
9 2.2 0.1
Node c
A
P
Mea
n lcb
Std
Dev.
4 4 0.4
6 9.7 0.3
9 2.2 0.1
Node e

calibration nodes
Point P
A
P
Mea
n lcb
Std
Dev.
2 4 0.4
5 9.7 0.3
9 2.2 0.1
Node b
2
)2,24,2( −
We only calculate the distance
using the same AP’s
44
A
P
Mean Std
Dev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
9 2.2 0.1
A
P
Mea
n lcb
Std
Dev.
4 4 0.4
7 9.7 0.3
9 2.2 0.1
Node c
A
P
Mea
n lcb
Std
Dev.
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( −+−+−

calibration nodes
Number of
coincidents AP
45
2
1
, )()( j
r
j
jiii plcllNPl ∑ −==−
=

Location: 8 to divide by the number of coincidents
AP’s
Point P
A
P
Mea
n lcb
Std
Dev.
2 4 0.4
5 9.7 0.3
9 2.2 0.1
Node b
1
)2.24.2( 2
−
46
A
P
Mean Std
Dev.
4 5.1 0.5
7 3.1 0.1
9 2.4 0.1
9 2.2 0.1
A
P
Mea
n lcb
Std
Dev.
4 4 0.4
7 9.7 0.3
9 2.2 0.1
Node c
A
P
Mea
n lcb
Std
Dev.
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.
47
A
P
Mean Std
Dev.
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.
48
A
P
Mean Std
Dev.
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.
2.2
1
6.0
1
2.0
1
2.26.02.0;
2.2
1
6.0
1
2.0
1
2.26.02.0;
2.2
1
6.0
1
2.0
1
2.26.02.0
++
++
=
++
++
=
++
++
=
cebcebceb zzz
z
yyy
y
xxx
x

Location: 9 to calculate the position
∑
∑
∑
∑
∑
∑ ===
===
q
i
q
q
i
q
q
i
q
l
z
z
l
y
y
l
x
x
1
1
;
1
1
;
1
1
49
∑
∑
∑
∑
∑
∑ =
=
=
=
=
=
i i
q
i i
i i
q
i i
i i
q
i i
l
l
l
l
l
l
1
1
1
1
1
1
111

1. Context
Index
4. Sensor Fusion
5. Test
50

51
4. Sensor Fusion

•Accelerometer
Sensor Fusion
•Magnetometer
52

Accelerometer: acceleration
Axis
∑−=
q j
i
ii
F
ga
53
∑=
−=
j
ii
m
ga
1

Accelerometer: acceleration
∑−=
q j
i
ii
F
ga
Gravity Force
Acceleration
54
∑=
−=
j
ii
m
ga
1
Axis Force
identifier
Mass of the
device
Acceleration

Accelerometer: linear acceleration
i
q j
i
ii g
F
ga −−= ∑'
55
i
j
ii g
m
ga −−= ∑=1
'

Accelerometer: position calculation
r0
r1 r
2
01 1'·
2
1
tarr ∆+=
rrr
2
12 2'·
2
1
tarr ∆+=
rrr
56
O
r1 r2
r0 can be obtained from the GPS or from a calibration node.

Accelerometer: position calculation
)·'
1
( 2
1 kk
n
k tarr ∆+= ∑ −
rrr
57
)·'
2
(
1
1 kk
k
k tarr ∆+= ∑=
−

Accelerometer: axes y
58
x
z

Accelerometer: axes y
59
x
z
True coordinate system depends on
the orientation or the smartphone

Accelerometer: axes transformation
y
North
East
y’
60
x
z
Altitude
z’
x’

Accelerometer + Magnetometer
Combination of both sensors allows to know
orientation of the smartphone
61

Accelerometer: orientation matrix
y North
East
x’
y’
0º
y
62
Altitude
z’
x’y
90º
y
180º
y
270º

1. Context
Index
4. Sensor Fusion
5. Test
63

64
5. Test

• RSS
Test
• Galaxy Nexus III
• Android 4.3
• Dual Core 1.2 GHz
• 1 Gb RAM
• Sensor Fusion
65

• 40 nodes
• 120 m2
• kmax=15
RSS Test: Building A (Flat)
• Threshold=3σ=6.18
• 100 tests
66

• 40 nodes
• 120 m2
• kmax=15
• Threshold=3 σ =6.18
• 100 tests
Maximum precision: 1,5 m
67

• 40 nodes
• 1,600 m2
• kmax=15
• 100 tests
Maximum precision: 1,5 m
68

• 40 nodes
• 1,600 m2
• kmax=15
• 100 tests
Maximum precision: 5 m
69

• 2000 measures to study
reliability
• 1,600 m2
Sensor Fusion Test
70

• 2000 measures to study
reliability
• 1,600 m2
Sensor Fusion Test
Error is higher
than 40% in only
10 m!!!
71

•High dependence on
frequency of sample
• Constant bias error of the
accelerometer: it increases
Sensor Fusion Test: Problems
accelerometer: it increases
even in static position
72

1. Context
Index
4. Sensor Fusion
5. Test
73

74

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
75
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.
76
• 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.

RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones

RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones

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RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones