1. AN IMU/MAGNETOMETER-BASED INDOOR POSITIONING SYSTEM USING KALMAN FILTERING
Hendrik Hellmers, Abdelmoumen Norrdine ,
Jorg Blankenbach and Andreas Eichhorn
Technische Universität Darmstadt, RWTH Aachen University
Germany
2013 International Conference on Indoor Positioning
and Indoor Navigation
임형섭

2. Outline
Motivation
Design Details
Simulation
Result

3. Motivation Improve localization accuracy
IMU have drifting and non-linearity error -> combine
Problem
-Multipath fading
-Temperature and humidity effect
-Attenuation of wall and floor
-People or moving objects effect
Solution: Magnetometer
-Penetrate building materials without attenuation,
fading, multipath or signal delay

4. Design details MILPS(Magnetic Indoor Local Positioning System)
IMU(Inertial Measurement Unit)
EKF(Expanded Kalman Filter)

5. MILPS
Reference stations consisting of magnetic coils with known positions
Mobile station(user)
Capturing the B of multiple coils
Swiching coil’s current direction to eliminate low frequency noise
Time division multiplexing
Blurring effect

6. MILPS

7. IMU
Consisting of 3-axial accelerometer, gyroscope, magnetometer and pressure sensor
Sample rate up to 2.4kHz

8. IMU
Position:
State(for EKF):

9. EKF-wikipedia Prediction Update State measurement Covariance
Innovation
Innovation covar
Kalman gain
Updated state
Updated covar

10. EKF-this paper Prediction Update State measurement Covariance
Innovation
Innovation covar
Kalman gain
Updated state
Updated covar

11. EKF

12. Simulation Track starts at position 1 and end at point 13 2 Coil
IMU sample rate:200Hz
Magnetometer sample rate:1Hz&2Hz
Average speed: 0.8m/s
Mark with a small permanent magnet when passing existing track points(observation to coil A at 3,5,7,9,11)
Combine MILPS & low-cost IMU(EKF)
Error free&

13. Test bed

14. Result

15. Result

16. Result