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. Context

2. Indoor positioning

3. Wifi Fingerprinting

4. Sensor Fusion

5. Test

6. Conclusions and Future Work

1. Context

• Location Based Systems

• Context Awarerecommendation systems

Context

recommendation systems

Need location!

Three main ítems about location:

• Coverage

Location

• Precision

• Security

Three main ítems about location:

• Coverage

Location

• Precision

• Security

GNSS are the mainlocation systems…

Coverage

GNSS are the mainlocation systems…

Coverage

… but GNSS only workoutdoor

What happens indoor?

Coverage

How can we get the position indoor?

Question

•We only want to use infraestructuresalready installed

• The pass from outdoor to indoor

Our restrictions

• The pass from outdoor to indoorenvironment should have to betransparent to the user

• The positioning should have to beperformed with the smartphone

•We only want to use infraestructures already installed

• The pass from outdoor to indoor environment should have to betransparent to the user

Our restrictions

• The positioning should have to beperformed with the smartphone

• Without internet connection• End user application

Movistar and Vodafone 3G and 2G coverage

Why without internet connection?

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

1. Context

4. Sensor Fusion

5. Test

2. Indoor Positioning

• Markers

• Wireless systems

How to get indoor positioning?

• Inertial systems

• Markers distributed within the building (like QR codes).

• Easy and cheap to install

Markers

• User and environment dependent

• Is not a true positioning system

• 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

• 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.

• Markers

Our approach

• Markers

Our approach

• Markers

Our approach

• Markers

Our approach

1. Context

4. Sensor Fusion

5. Test

• Calibration

RSS Fingerprinting

• Positioning

Calibration: 1. Create a matrix of nodes

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

Calibration: 3 measure signal level from each AP at every node…

Node b

Levelcalibration

Calibration: 3 measure signal level from each AP at every node… several times

Node b

lcb,1 lcb,2 lcb,3 lcb,4 lcb,5

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

]],...,[],...,,...,[],...,,...,[[)(,,,,1,1,i kikijijiii

lclclclclclcnN =

Nodeidentifier

APidentifier

APmeasured

Calibration: 4 Calculate the mean and the standard deviation

Node b

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

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

=, 2)(

1 nr cllc∑ −=σ

1,, )(

rjiji cllc

n∑ −

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

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

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 ∈∈=

],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.

• 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

Location: 1 to take several measures

a b 543

Location: to do as in calibration steps 3 to 6

Point P

Mean StdDev.

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

]],...,[],...,,...,[],...,,...,[[)(,,,,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

Mean lc b

StdDev.

2 4 0.4

5 9.7 0.3

9 2.2 0.1

Node b

Point P

Mean StdDev.

4 5.1 0.5

7 3.1 0.1

9 2.4 0.1

9 2.2 0.1

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

Point P

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

Mean StdDev.

4 5.1 0.5

7 3.1 0.1

9 2.4 0.1

9 2.2 0.1

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( −+−+−

Number of coincidents AP

1, )()( j

jjiii plcllNPl ∑ −==−

Location: 8 to divide by the number of coincidentsAP’s

Point P

Mean lc b

StdDev.

2 4 0.4

5 9.7 0.3

9 2.2 0.1

Node b

)2.24.2( 2−

Mean StdDev.

4 5.1 0.5

7 3.1 0.1

9 2.4 0.1

9 2.2 0.1

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

)2.24.2()41.5( 22 −+−

)2.24.2()7.91.3()41.5( 222 −+−+−

Location: 9 to calculate the distance to nodes

Point P

)2.24.2( 2−Distance P-b=0.2 a.u.

Mean StdDev.

4 5.1 0.5

7 3.1 0.1

9 2.4 0.1

)2.24.2()41.5( 22 −+−

)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

)2.24.2( 2−Distance P-b=0.2 a.u.

Mean StdDev.

4 5.1 0.5

7 3.1 0.1

9 2.4 0.1

)2.24.2()41.5( 22 −+−

)2.24.2()7.91.3()41.5( 222 −+−+−

Distance P-e=0.6 Distance P-c=2.2 a.u.

2.26.02.0;

2.26.02.0

cebcebceb zzz

Location: 9 to calculate the position

∑∑

1. Context

4. Sensor Fusion

5. Test

4. Sensor Fusion

•Accelerometer

Sensor Fusion

•Magnetometer

Accelerometer: acceleration

∑−=q j

ii mga

Accelerometer: acceleration

∑−=q j

Gravity Force

Acceleration

ii mga

Axis Forceidentifier

Mass of thedevice

Acceleration

Accelerometer: linear acceleration

ga −−= ∑'

ga −−= ∑=1

Accelerometer: position calculation

r0 r1 r

201 1'·

1tarr ∆+= rrr

212 2'·

1tarr ∆+= rrr

r0 can be obtained from the GPS or from a calibration node.

Accelerometer: position calculation

( 21 kk

k tarr ∆+=∑ −rrr

k tarr ∆+=∑=

Accelerometer: axesy

True coordinate system depends onthe orientation or the smartphone

Accelerometer: axes transformationy

Altitude

Accelerometer + Magnetometer

Combination of both sensors allows to knoworientation of the smartphone

Accelerometer: orientation matrix

y North

Altitude

x’y90º

1. Context

4. Sensor Fusion

5. Test

• RSS

• Galaxy Nexus III• Android 4.3• Dual Core 1.2 GHz• 1 Gb RAM

• Sensor Fusion

• 40 nodes

• 120 m2

• kmax=15

RSS Test: Building A (Flat)

• Threshold=3σ=6.18

• 100 tests

• 40 nodes

• 120 m2

• kmax=15

• Threshold=3 σ =6.18

• 100 tests

Maximum precision: 1,5 m

• 40 nodes

• 1,600 m2

• kmax=15

• 100 tests

Maximum precision: 1,5 m

• 40 nodes

• 1,600 m2

• kmax=15

• 100 tests

Maximum precision: 5 m

• 2000 measures to study reliability

• 1,600 m2

Sensor Fusion Test

• 2000 measures to study reliability

• 1,600 m2

Sensor Fusion Test

Error is higher than 40% in only 10 m!!!

•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

1. Context

4. Sensor Fusion

5. Test

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

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.

• To study where is the origin of the error.

• To study how to avoid the building dependence on the error.

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