Navigine IPIN 2014

1. Efficient Localization Using Different
Mean Offset Models In Gaussian
Processes
V. Kosiantchouk, A. Panyov, A. Smirnov, A. Golovan

2. Importance of Indoor Navigation
• 90% of the time people spend indoors
• Navigation at the big locations
(hospitals, airports, malls)
• Satellite navigation systems are
unavailable
Problem appearance:
Solution: • Wi-Fi & Bluetooth LE signals
• Digital map of the building
• MEMS sensors
o Accelerometer
o Gyroscope
o Magnetometer
o Barometer
• Powerful processors
2/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

3. Wi-Fi/Bluetooth signals fingerprinting
• Collecting radio map of the building.
o Save coordinates of the reference
points(RP) to the database
o Save histogram of the signal at each RP
database of the
fingerprints
RSSI, SSID, BSSID
x, y, location Id
Measuring App.
radiomap
Training stage:
3/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

4. 4/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov
4
Accelerometer
Gyroscope
Magnetometer
Smartphone
sensors
Step Detection
Step Length
Estimation
Heading
Estimation
Pedestrian
Dead
Reckoning
Sensors
data
Navigation Stage

5. Particle Filter
Prediction: Correction:
• Step length - 풍
• Heading angle - 휽
Propagate particles
풌 = 풙풊
풙풊+ퟏ
풌 + 풍 ∗ 풄풐풔 (휽풊)
풌 = 풚풊
풚풊+ퟏ
풌 + 풍 ∗ 풔풊풏 휽풊
푷 풙풊 풙풊−ퟏ =
=
ퟎ, 풊풇 풂 풑풂풓풕풊풄풍풆 풄풓풐풔풔풆풅 풂 풘풂풍풍
ퟏ, 풐풕풉풆풓풘풊풔풆
• When signal solution is available
the weight of the particles must be
corrected.
풌 ] =
푷 풁 풊 풙 풊
ퟏ
ퟐ흅흈
풌−풁풊
ퟐ흈ퟐ ,
− 풙풊
풆
풁 풊 − device coordinates obtained by
processing of the RSSI measurements;
흈-variance of the measurement.
• Posterior probability
풌 = 풘풊 −ퟏ
• 풘풊
풌 푷 풁 풊 풙풊 ] 푷 풙풊 풙풊−ퟏ
• Normalize weights
• Posterior distribution:
푵 풘풊
• 푷 풙풊 풁ퟎ:풊 = 풌=ퟏ
풌 휹 풙풊 − 풙풊
풌
5/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

6. Gaussian processes for signal modeling
Predicted mean
of the signal strength (in dB)
Predicted variance
of the signal strength (in dB2)
6/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

7. Gaussian processes for signal modeling
Predicted mean
of the signal strength (in dB)
푷 풛 풙풊 ]
=
ퟏ
ퟐ흅흈( 풙풊 )
풆
− 풛−μ 풙풊
ퟐ
ퟐ흈ퟐ( 풙풊 )
풙풊 - coordinates of i-th particle.
풛 - 퐑퐒퐒 퐟퐫퐨퐦 퐭퐡퐞 퐀퐏.
μ 풙풊 - mean and
흈ퟐ( 풙풊 ) - variance of the signal
predicted by GP at point 풙풊 .
Correction using GP:
7/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

8. Introducing mean offset model to GP
Zero-Mean Gaussian
μ 퐱′ = 퐠(퐗, 퐅, σ풏, σ풇, 퐥, 퐱′)
Gaussian With Mean offset model
μ 퐱′ = 퐬 퐩ퟏ, … , 퐩퐧, 퐱′ + 퐠(퐗, 퐅′, σ풏, σ풇, 퐥, 퐱′)
F’= F – s(X)
8/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

9. GP Prediction maps in existing works
Hsiao-Chieh Yen, Chieh Chin Wang.
Adapting Gaussian Processes For
Cross-Device
Wi-Fi Localization
R. M. Faragher, C. Sarno, M. Newman.
Opportunistic Radio SLAM for
Indoor Navigation
using Smartphone Sensors
9/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

10. Constant mean offset model
Constant model parameter μ > -100 dB
Visibility Area of one BLE beacon Map of GP mean prediction
10/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

11. Constant model parameter μ < -100 dB
Constant mean offset model
GP mean prediction with μ > -100 dB GP mean prediction with μ < -100 dB
11/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

12. Constant mean offset model
Example №1
Drawbacks of constant mean parameter < -100 dB
Here we manually deleted 2 RPs from the radiomap at the left elevator corridor
12/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

13. Drawbacks of constant mean parameter < -100 dB
GP mean prediction built on full
set of Reference points
Constant mean offset model
Example №1
GP mean prediction built on reduced
set of Reference points
13/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

14. Constant mean offset model
Example №1
Test Trace
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15. Constant mean offset model
GP Const perfomance Example №1
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16. Proposed mean offset models
Log distance
mean offset model
풔 풙 = 푨 − 푩 log( 풙 − 풙푨푷 )
Linear distance
mean offset model
풔 풙 = 푨 − 푩 × 풙 − 풙푨푷
풔 풙 − 풓풆풄풆풊풗풆풅 풔풊품풏풂풍 풔풕풓풆풏품풕풉 풂풕 풑풐풊풏풕 풙
푨 − 풔풊품풏풂풍 풔풕풓풆풏품풕풉 풂풕 ퟏ풎 풇풓풐풎 풕풉풆 푨풄풄풆풔풔 푷풐풊풏풕
푩 − 풂풕풕풆풏풖풂풕풊풐풏 풑풂풓풂풎풆풕풆풓
풙풄풐풐풓풅풊풏풂풕풆풔 풐풇 풂풏 푨풄풄풆풔풔 푷풐풊풏풕
푨푷 −
16/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

17. Advantages of the proposed models
Effective RSS prediction near AP
o Proposed models quite accurately predict signal strength in the area near
the Access Point
Effective prediction of visibility area
o Proposed models predict high signal strength near AP and low signal
strength at the distance, even in absence of reference points in the area.
Informative parameters
o Term A provides information about the power of the transmitter.
o Term B reflects the influence of the building structure on signal
attenuation near the Access Point
o Term 풙푨푷 gives the position of the transmitter
17/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

18. Comparison of Constant and Log-distance models
18/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

19. Comparison of Constant and Log-distance models
Full set of Reference Points
GP mean prediction built using
Constant mean offset model
on full set of Reference points
GP mean prediction built using
Log mean offset model
on full set of Reference points
19/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

20. Comparison of Constant and Log-distance models
Reduced set of Reference Points
GP mean prediction built using
Constant mean offset model
on reduced set of Reference points
GP mean prediction built using
Log mean offset model
on reduced set of Reference points
20/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

21. Comparison of Constant and Log-distance models
Test Trace Example №1
21/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

22. Comparison of Constant and Log-distance models
GP Const perfomance Example №1
22/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

23. Comparison of Constant and Log-distance models
GP Log perfomance Example №1
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24. Priority of the Log model over Linear
Better correspondence to the nature
of the signal propagation
More precise estimation of AP positions
More precise prediction of visibility area
24/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

25. Comparison of predicted visibility areas
Area of predicted mean below -100 dB
is marked as blue layer
Linear model results Log model results
25/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

26. Comparison of AP positions estimation accuracy
Linear model results Log model results
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27. Comparing performance of methods
Full set of RPs (50)
Full set of APs (10 Wi-Fi + 20 BLE beacons )
27/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

28. Comparing performance of methods
Test Trace
28/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

29. Comparing performance of methods
Histograms
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30. Comparing performance of methods
Histograms
GP Mean Constant
30/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

31. Comparing performance of methods
GP Mean Linear
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32. Comparing performance of methods
GP Mean Log
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33. Comparing performance of methods
Full set of RPs (50)
Reduced set of APs (10 BLE beacons )
33/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

34. Comparing performance of methods
Test Trace
34/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

35. Comparing performance of methods
Histograms
35/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov

36. Comparing performance of methods
GP Mean Constant
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37. Comparing performance of methods
GP Mean Linear
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38. Comparing performance of methods
GP Mean Log
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39. Low visibility area of BLE Beacons
Visible area for one Wi-Fi transmitter
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40. Low visibility area of BLE Beacons
Visible area for BLE beacons
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41. 41
Thank you!
Contacts
Vasili Kosianchouk
v.kosyanchuk@navigine.ru
www.navigine.com
Try our indoor navigation
platform at
http://client.navigine.com
Dynamic test results
41/27 Efficient Localization Using Different Mean Offset Models In Gaussian Processes V. Kosiantchouk, A. Panyov, A. Smirnov