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

Outline Motivation Design Details Simulation Result

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

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

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

MILPS

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

IMU Position: State(for EKF):

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

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

EKF

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&

Test bed

Result

Result

Result