Ultrasonic imaging has a wide range of applications, e.g., in navigation or healthcare. Compared to medical use cases, airborne sensor solutions face a severe problem: The reduced speed of sound in air and the requirement for large coverage areas provoke large propagation delays which slow down imaging frame rates.
This shortcoming motivates the use of sparse sampling concepts which provide high imaging accuracy with only a few snapshot measurements. Your goal is to further develop calibration techniques based on tensor decomposition that exploit the low-rank structure of imaging data.
- Preliminary work on this topic has been done in MATLAB. The programming language you use to implement your work is up to you.
- No special prerequisites are required but basic knowledge in optimization, matrix analysis and sensor array processing can be a plus.
- Real measurement data is available to work with. Additionally, you can conduct measurements in cooperation with the MUST group led by Prof. Dr. mont. Mario Kupnik to test and verify your proposed methods.
The topic is suitable for various works, such as seminars, bachelor's or master's theses and can be individually adapted to your interests. Please contact Raphael Müller (firstname.lastname@example.org-…) for more information.