/Hybrid SAR/ISAR for Networks of Radars

Hybrid SAR/ISAR for Networks of Radars

PhD - Leuven | More than two weeks ago

You will try to merge two a-priori incompatible radar imaging techniques

In autonomous vehicles, the mm-wave radar is a key technology thanks to its robustness in harsh weather and extreme light conditions, among other advantages. Moreover, current commercial-off-the-shelf radars, referred to as 4D imaging radars, are capable of providing high-resolution point clouds of the environment, thanks to their high bandwidth as well as their large antenna array. Recently, IMEC also introduced its multiple-input-multiple-output (MIMO) 140GHz radar with a bandwidth as large as 10GHz.


However, the obtained angular resolution offered by such radars is still limited. To increase the angular resolution, the size of the antenna array needs to be increased, resulting in higher complexity in the receiver structure, higher demand of memory and computer resources, and finally, a higher cost and power consumption. Furthermore, each radar has a limited field of view. A promising solution to achieve higher angular resolution is to use a network of radars.


On the other hand, radar imaging algorithms, namely synthetic aperture radar (SAR) and inverse SAR (ISAR), are conventionally used in many applications, including recently in automotive applications. SAR / ISAR can provide high-resolution point clouds of the targets / environment at the cost of a higher computational complexity. These techniques are usually not used jointly because SAR needs stationary targets and a moving radar while the opposite is needed for ISAR.


Using the IMEC radar technology, the goals of this PhD are:

  • To propose radar imaging algorithms for a network of radars. Both centralized and distributed SAR / ISAR processing need to be evaluated and compared in order to get an optimum processing configuration;
  • To implement a hybrid SAR / ISAR scheme in order to achieve a novel algorithm capable of generating point cloud of targets with reduces motion limitations;
  • To develop appropriate efficient machine-learning-based algorithms to reduce the complexity of the hybrid SAR / ISAR algorithm.

The proposed solutions will be validated first by simulation then with real radar measurements.


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 shape future radar sensors. You will publish your research in top-level journals and conferences.

Required background: Signal processing; familiarity with deep learning approaches. Knowledge in multi-antennas signal processing. Proficiency with Matlab or python is a must. Some knowledge of radar concepts, optimization and compressive sensing is a plus.

Type of work: 20% literature/theory, 60% modelling/simulation, 20% experimental

Supervisor: Hichem Sahli

Daily advisor: Seyed Hamed Javadi

The reference code for this position is 2023-091. Mention this reference code on your application form.

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