Short Description

While radio-based positioning can provide an absolute position, it often lacks the reliability and robustness of relative position schemes. Relative positioning can be performed with inertial measurement units (IMU), e.g., accelerometer, gyroscope, which measure only a relative offset to the current position. By combining both techniques it is possible to provide accurate, reliable and robust positioning.

In the Christian Doppler laboratory for location-aware electronic systems one research focus is positioning in a convenience store. This is an especially challenging task, as the radio propagation is characterized by rich scattering. Thus, a combined approach of radio-based and IMUs is necessary.

To be able to jointly use both, radio-based and relative positioning, sensor fusion is needed. This can be accomplished with filtering techniques as a Kalman filter or particle filters. Your task is to review the literature, implement state of the art algorithms and validate the algorithms with synthetic and measured data.

Your Tasks

• implement tracking filters
• validate the algorithms with synthetic data
• analyze measured radio channels with the algorithms
• (optional / if time) extend the algorithms and (co-)author a scientific publication

Your Profile

• motivation and reliablility are a prerequisite
• basic knowledge in Matlab programming
• knowledge in wireless communicaiton is beneficial (mobile radio systems)