PhD - Leuven | More than two weeks ago
Radar is a key technology for applications requiring precise detection in 3D space with a small sensor as in autonomous driving cars. Due to their high carrier frequency, mm-wave radars can use large bandwidth, which gives access to high range resolution, and have small antennas, which allows Multiple-input-multiple-output (MIMO) solutions to estimate the angular position of the obstacles. The combination of high range and angular resolution with the ability to measure the Doppler effect provides accurate information on the surrounding environment.
However, conventional radars require a large antenna aperture to achieve the required high angular resolution. This impacts the hardware complexity. In addition, a large amount of obstacles in the radar environment may produce significant multipath propagations. This results in the apparition of ghost targets in the radar measurement. The use of multiple radars on a vehicle can solve this problem and improves the measurement accuracy and resolution at the same time. With a clever design of each individual radar as well as of the radar network topology, the overall radar network can outperform the performances of single radars.
The goal of this PhD is to exploit the full potential of this approach to improve the system resolution, reduce the complexity of each radar individually and remove the ghost targets due to multi-path propagation. The student will propose waveforms and algorithms to optimize the time and frequency resources sharing between radars. Multi-radar fusion and image reconstruction algorithms will also be proposed to combine all radar measurements in a single high-resolution representation of the environment. In addition, the student will investigate implementation challenges such as synchronization between radars and impact/mitigation of hardware non-idealities. The proposed solutions will be validated first by simulation then with real radar measurements. If time allows, this radar network concept will also be adapted to other environments such as robotics, smart building or smart factory.
The successful PhD candidate will be part of a large IMEC team working on the research, implementation and prototyping of future radar systems, composed of experts in digital and analog mm-wave design, radar and wireless communication systems, signal processing and machine learning algorithms. This is a unique opportunity to actively contribute and develop breakthrough technology and build-up future radar sensors. You will publish your research in top-level journals and conferences.
Required background: Signal processing for wireless (communication or radar). Knowledge in multi-antennas signal processing. Proficiency with Matlab or Python. Some knowledge of radar concepts, optimization and compressive sensing is a plus.
Type of work: 10% literature/theory, 70% modelling/simulation, 20% experimental
Supervisor: Hichem Sahli
Daily advisor: Marc Bauduin
The reference code for this position is 2023-093. Mention this reference code on your application form.