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RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones

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Location-based applications require knowing the user position constantly in order to find out and provide information about user’s context. There is a need for new methods that calculate the location of users in indoor environments using smartphone sensors. In this presentation the authors (Laia Descamps-Vila, Antoni Perez-Navarro, Jordi Conesa) address that problem by presenting two methods that estimate the user position through a smartphone.

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RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones

  1. 1. 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
  2. 2. 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
  3. 3. RSS and sensor fusion algorithms for indoor location systems on smartphones 3 1. Context
  4. 4. • Location Based Systems • Context Aware recommendation systems Context RSS and sensor fusion algorithms for indoor location systems on smartphones recommendation systems 4 Need location!
  5. 5. Three main ítems about location: • Coverage Location RSS and sensor fusion algorithms for indoor location systems on smartphones • Precision • Security 5
  6. 6. Three main ítems about location: • Coverage Location RSS and sensor fusion algorithms for indoor location systems on smartphones • Precision • Security 6
  7. 7. GNSS are the main location systems… Coverage RSS and sensor fusion algorithms for indoor location systems on smartphones 7
  8. 8. GNSS are the main location systems… Coverage RSS and sensor fusion algorithms for indoor location systems on smartphones 8 … but GNSS only work outdoor
  9. 9. What happens indoor? Coverage RSS and sensor fusion algorithms for indoor location systems on smartphones 9
  10. 10. How can we get the position indoor? Question RSS and sensor fusion algorithms for indoor location systems on smartphones 10
  11. 11. •We only want to use infraestructures already 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 indoor environment should have to be transparent to the user • The positioning should have to be performed with the smartphone 11
  12. 12. •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 RSS and sensor fusion algorithms for indoor location systems on smartphones • The positioning should have to be performed with the smartphone • Without internet connection • End user application 12
  13. 13. Movistar and Vodafone 3G and 2G coverage Why without internet connection? RSS and sensor fusion algorithms for indoor location systems on smartphones 13 Source: www.sensorly.com
  14. 14. •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 14
  15. 15. 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
  16. 16. RSS and sensor fusion algorithms for indoor location systems on smartphones 16 2. Indoor Positioning
  17. 17. • Markers • Wireless systems How to get indoor positioning? RSS and sensor fusion algorithms for indoor location systems on smartphones • Wireless systems • Inertial systems 17
  18. 18. • 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 18
  19. 19. • 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 19
  20. 20. • 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. 20
  21. 21. • Markers • Wireless systems Our approach RSS and sensor fusion algorithms for indoor location systems on smartphones • Wireless systems • Inertial systems 21
  22. 22. • Markers • Wireless systems Our approach RSS and sensor fusion algorithms for indoor location systems on smartphones • Wireless systems • Inertial systems 22
  23. 23. • Markers • Wireless systems Our approach WIFI RSS and sensor fusion algorithms for indoor location systems on smartphones • Wireless systems • Inertial systems 23 WIFI
  24. 24. • Markers • Wireless systems Our approach WIFI RSS and sensor fusion algorithms for indoor location systems on smartphones • Wireless systems • Inertial systems 24 WIFI
  25. 25. 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 25
  26. 26. RSS and sensor fusion algorithms for indoor location systems on smartphones 26 3. Wifi Fingerprinting
  27. 27. • Calibration RSS Fingerprinting RSS and sensor fusion algorithms for indoor location systems on smartphones • Positioning 27
  28. 28. Calibration: 1. Create a matrix of nodes 1 2 RSS and sensor fusion algorithms for indoor location systems on smartphones 28 c a d b e 5 7 43 6 9 8
  29. 29. 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 RSS and sensor fusion algorithms for indoor location systems on smartphones 29 5 7 43 6 9 8 c a d b e
  30. 30. Calibration: 3 measure signal level from each AP at every node… Node b A P Level calibration 2 4 RSS and sensor fusion algorithms for indoor location systems on smartphones 30 2 4 4 9 5 10 6 1 7 5 9 3
  31. 31. Calibration: 3 measure signal level from each AP at every node… several times Node b A P lcb,1 lcb,2 lcb,3 lcb,4 lcb,5 RSS and sensor fusion algorithms for indoor location systems on smartphones 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
  32. 32. Calibration: 3 measure signal level from each AP at every node… several times ]],...,[],...,,...,[],...,,...,[[)( 111 nnn i lclclclclclcnN = Number of measures Level of calibration RSS and sensor fusion algorithms for indoor location systems on smartphones 32 ]],...,[],...,,...,[],...,,...,[[)( ,,,,1,1,i kikijijiii lclclclclclcnN = Node identifier AP identifier AP measured
  33. 33. 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 RSS and sensor fusion algorithms for indoor location systems on smartphones 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
  34. 34. Calibration: 4 Calculate the mean and the standard deviation lc n r ji cl ∑ = , 2 )( 1 n r cllc∑ −=σ RSS and sensor fusion algorithms for indoor location systems on smartphones 34 nji r ji cl ∑ = =1 , , 2 , 1 ,, )( 1 ji r r jiji cllc n ∑ − − = = σ
  35. 35. 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 RSS and sensor fusion algorithms for indoor location systems on smartphones 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
  36. 36. 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 RSS and sensor fusion algorithms for indoor location systems on smartphones 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
  37. 37. Calibration Matrix ],0(;],0(_ kjsiclmatrixCal ∈∈= RSS and sensor fusion algorithms for indoor location systems on smartphones 37 ],0(;],0(_ , kjsiclmatrixCal ji ∈∈=
  38. 38. 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. RSS and sensor fusion algorithms for indoor location systems on smartphones 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
  39. 39. Location a b 543 1 2 P RSS and sensor fusion algorithms for indoor location systems on smartphones 39 c d e 76 9 8 P
  40. 40. Location: 1 to take several measures a b 543 1 2 RSS and sensor fusion algorithms for indoor location systems on smartphones 40 c d e 76 9 8 P
  41. 41. Location: to do as in calibration steps 3 to 6 Point P A P Mean Std Dev. 4 5.1 0.5 RSS and sensor fusion algorithms for indoor location systems on smartphones 41 4 5.1 0.5 7 3.1 0.1 9 2.4 0.1
  42. 42. Location: to do as in calibration steps 3 to 6 ]],...,[],...,,...,[],...,,...,[[)( 111 nnn lplplplplplpnP = Number of measures Level of position RSS and sensor fusion algorithms for indoor location systems on smartphones 42 ]],...,[],...,,...,[],...,,...,[[)( ,,,,1,1, mimijijiii lplplplplplpnP = Node identifier AP identifier AP measured (≠ k)
  43. 43. 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 RSS and sensor fusion algorithms for indoor location systems on smartphones 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
  44. 44. Location: 7 to calculate the “euclidean distance” to 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 RSS and sensor fusion algorithms for indoor location systems on smartphones 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( −+−+−
  45. 45. 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 45 2 1 , )()( j r j jiii plcllNPl ∑ −==− =
  46. 46. 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 − RSS and sensor fusion algorithms for indoor location systems on smartphones 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 −+−+−
  47. 47. 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 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.
  48. 48. 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 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
  49. 49. 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 RSS and sensor fusion algorithms for indoor location systems on smartphones 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
  50. 50. 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
  51. 51. RSS and sensor fusion algorithms for indoor location systems on smartphones 51 4. Sensor Fusion
  52. 52. •Accelerometer Sensor Fusion RSS and sensor fusion algorithms for indoor location systems on smartphones •Magnetometer 52
  53. 53. Accelerometer: acceleration Axis ∑−= q j i ii F ga RSS and sensor fusion algorithms for indoor location systems on smartphones 53 ∑= −= j ii m ga 1
  54. 54. Accelerometer: acceleration ∑−= q j i ii F ga Gravity Force Acceleration RSS and sensor fusion algorithms for indoor location systems on smartphones 54 ∑= −= j ii m ga 1 Axis Force identifier Mass of the device Acceleration
  55. 55. Accelerometer: linear acceleration i q j i ii g F ga −−= ∑' RSS and sensor fusion algorithms for indoor location systems on smartphones 55 i j ii g m ga −−= ∑=1 '
  56. 56. Accelerometer: position calculation r0 r1 r 2 01 1'· 2 1 tarr ∆+= rrr 2 12 2'· 2 1 tarr ∆+= 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.
  57. 57. Accelerometer: position calculation )·' 1 ( 2 1 kk n k tarr ∆+= ∑ − rrr RSS and sensor fusion algorithms for indoor location systems on smartphones 57 )·' 2 ( 1 1 kk k k tarr ∆+= ∑= −
  58. 58. Accelerometer: axes y RSS and sensor fusion algorithms for indoor location systems on smartphones 58 x z
  59. 59. Accelerometer: axes y RSS and sensor fusion algorithms for indoor location systems on smartphones 59 x z True coordinate system depends on the orientation or the smartphone
  60. 60. Accelerometer: axes transformation y North East y’ RSS and sensor fusion algorithms for indoor location systems on smartphones 60 x z Altitude z’ x’
  61. 61. Accelerometer + Magnetometer Combination of both sensors allows to know orientation of the smartphone RSS and sensor fusion algorithms for indoor location systems on smartphones 61
  62. 62. Accelerometer: orientation matrix y North East x’ y’ 0º y RSS and sensor fusion algorithms for indoor location systems on smartphones 62 Altitude z’ x’y 90º y 180º y 270º
  63. 63. 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
  64. 64. RSS and sensor fusion algorithms for indoor location systems on smartphones 64 5. Test
  65. 65. • 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 65
  66. 66. • 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 66
  67. 67. • 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
  68. 68. • 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 68
  69. 69. • 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 69
  70. 70. • 2000 measures to study reliability • 1,600 m2 Sensor Fusion Test RSS and sensor fusion algorithms for indoor location systems on smartphones 70
  71. 71. • 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!!! 71
  72. 72. •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 72
  73. 73. 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
  74. 74. RSS and sensor fusion algorithms for indoor location systems on smartphones 74 5. Conclusions and Future Work
  75. 75. 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 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.
  76. 76. 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 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.

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