Optimal Filtering with Kalman Filters and Smoothers Using AndroSensor IMU Data

1. Final Presentation
Group 4
Achmad Junaidi
Muhammad Irsyadi Firdaus
Avrilina Luthfil Hadi
Rani Fitri Febriyanti
Inertial Survey and Navigation System
Thursday, June 28 2018
Optimal Filtering with Kalman Filters and Smoothers
Using Andro Sensor IMU Data

2. Process
Data
Recording
Result
Data
processing
2
Analysis
IMU GPS Filtering and smoothing
Conclusion
Filtering and smoothing
Heading
Vertical and horizontal position

3. Instrument
Androsensor Application
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◉ GPS
◉ IMU
○ Accelerometer
○ Gyroscope
IMU Android 4.4.4 (KitKat) at Sony Z3
mobile phone

4. Data recording
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GPS Data IMU Data
GPS Time
latitude
longitude
altitude
stdE (m) stdU (m)
stdN (m)
GPS Time Acceleration (m/s)
Gyroscope (rad)

5. Recorded data result
◉ GPS
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IMU sampling rate : 100 Hz
Minimal 1/10 data GPS
Data = 78.000 X 1/10
= 7.800
20.8045 data
GPS data < IMU data

6. Recorded data result
◉ IMU
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IMU sampling rate : 100 Hz
Interval recording : 0.01 Sec

7. Matlab Processing data
◉ Data changed :
1. Time.interval
2. Time.dt
3. Time.zuptInterval
4. OBS.gpsR
5. OBS.zuptR
6. iniLlh
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8. Results (1)
◉Indicate the environtment
when GPS was recording the
data
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◉The pattern can be determine
the equipment position
Underground parking lot
Open spaceStart point
End point

9. Results (2)
◉ The GPS data will recording
continuously even the
equipment stop
◉ Remove several GPS data
(GPS data < IMU data)
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10. Results (3)
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11. Discussion
◉ Case 1 (Inside the Building)
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12. Discussion
◉ Case 2 (on the Open Area)
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13. Discussion
◉ Case 3 (on the Crossroad)
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14. Discussion
◉ Filtering and smoothing
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Filtering
Smoothing

15. Conclusions
◉ GPS data was good enough to record the point position while it had the best
performance on the crossroad and on the open area while the data was not good inside
of the building
◉ The filtered-smoothed IMU data had the best performance inside the tunnel when the
GPS data was not available and had better performance inside the building, on the
crossroad and on the open area than filtered-IMU data.
◉ Although the filtered-smoothed IMU data had better performance than filtered-IMU
data, those data still had some patches points that might be reduced by changing the
IMU parameters in the Kalman filter process.
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16. Thanks!
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