An IMU/magnetometer-based Indoor positioning system using Kalman filtering

Many infrastructure-based indoor positioning technologies such as UWB, WLAN, ultrasonic or infrared are limited by disturbances and errors caused by building objects (e.g. walls, ceiling and furniture). Magnetic fields, however, are able to penetrate various obstacles - in this case commonly used (building) materials - without attenuation, fading, multipath or signal delay. Thus, in the past years a DC Magnetic signal based Indoor Local Positioning System (MILPS), which consists of multiple electrical coils as reference stations and tri-axial magnetic sensors as mobile stations was developed. By observing magnetic field intensities of at least three different magnetic coils, position estimation of the magnetic sensors can be carried out even in severe indoor environments. However, the positioning algorithm currently used is designed for stop-and-go localization. This contribution focuses on the integration of a low cost Inertial Measurement Unit (IMU) in order to improve the system's positioning update rate and therefore provide complete 2D localization estimates for kinematic applications and probably afford position solutions even outside the coverage area of MILPS. Therefore an Extended Kalman-Filter (EKF) is adapted for position estimation. The filtering process is accomplished in two steps. The first step leads to position prediction caused by inertial data, which could be updated at the second step by using the MILPS-measurements. In this context simulations combining MILPS and IMU have been performed. Testing of the filter with real IMU-data and simulated MILPS positioning data delivered promising results for indoor positioning purposes.