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FI-NAL.Lab
AN IMU/MAGNETOMETER-BASED INDOOR POSITIONING SYSTEM US-
ING KALMAN FILTERING
Hendrik Hellmers, Abdelmoumen Norrdine ,Jorg Blankenbach and Andreas Eichhorn
Technische Universität Darmstadt, RWTH Aachen UniversityGermany
2013 International Conference on Indoor Positioning and Indoor Navigation
임형섭
FI-NAL.LabOutline
• Motivation
• Design Details
• Simulation
• Result
FI-NAL.LabMotivation
• Improve localization accuracy
• IMU have drifting and non-linearity error -> com-bine
• 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
FI-NAL.LabDesign details
• MILPS(Magnetic Indoor Local Positioning System)
• IMU(Inertial Measurement Unit)
• EKF(Expanded Kalman Filter)
FI-NAL.LabMILPS
• 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 fre-
quency noise• Time division multiplexing• Blurring effect
FI-NAL.LabMILPS
FI-NAL.LabIMU
• Consisting of 3-axial accelerometer, gyroscope, magnetometer and pressure sensor
• Sample rate up to 2.4kHz
FI-NAL.LabIMU
Position:
State(for EKF):
FI-NAL.LabEKF-wikipedia
State measurement
• Prediction State Covariance
• Update Innovation Innovation covar Kalman gain Updated state Updated covar
FI-NAL.LabEKF-this paper
State measurement
• Prediction State Covariance
• Update Innovation Innovation covar Kalman gain Updated state Updated covar
FI-NAL.LabEKF
FI-NAL.LabSimulation
• 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 pass-
ing existing track points(observation to coil A at 3,5,7,9,11)
• Combine MILPS & low-cost IMU(EKF)• Error free&
FI-NAL.LabTest bed
FI-NAL.LabResult
FI-NAL.LabResult
FI-NAL.LabResult
