Near-field interferometric ISAR (InISAR) with no constraint on radar motion
Leuven | More than two weeks ago
Recent advances in radar technology have enabled high-range resolution due to the large bandwidth available in mm-wave frequency technologies. However, achieving high angular resolution typically requires a large, impractical, or complex antenna. To overcome this challenge, radar imaging algorithms are employed. These algorithms synthetically enlarge the antenna aperture through either radar motion—leading to Synthetic Aperture Radar (SAR) algorithms—or by utilizing the target's motion, resulting in Inverse Synthetic Aperture Radar (ISAR) algorithms. In SAR, the radar moves, while any other motion would blur the image. Conversely, in ISAR, the radar remains stationary while capturing the image of a moving target [1]. Traditional ISAR algorithms also rely on the far-field assumption, where plane waves are assumed.
The motion constraints in current radar imaging algorithms challenge the growing adoption of mm-wave radars in applications such as automotive systems and robotics. The goal of this Ph.D. position is to push the boundaries of traditional imaging algorithms and develop radar imaging techniques free from motion constraints. These new techniques along with interferometric processing should enable capturing a dense, lidar-like point cloud of the environment and targets and be applicable in the near-field cases [2,3].
Required qualifications:
- An MSc in a field related to one or more: Electrical engineering, Computer Science, or Applied Computer Science.
- Strong signal
processing (statistical and digital) knowledge,
- Strong programming skills (Python).
- knowledge of radar concepts and SAR or ISAR is a plus.
By advancing state-of-the-art radar imaging techniques, this research will support several applications, including collaborative robots (co-bots), enhancing their ability to perceive and navigate complex environments with high precision, and broadening their potential across industries such as manufacturing and healthcare.
References:
[1] S. H. Javadi, A. Bourdoux, N. Deligiannis and H. Sahli, "Human Pose Estimation Based on ISAR and Deep Learning," in IEEE Sensors Journal, vol. 24, no. 17, pp. 28324-28337, 1 Sept.1, 2024, doi: 10.1109/JSEN.2024.3426030.
[2] J. W. Smith, M. E. Yanik and M. Torlak, "Near-Field MIMO-ISAR Millimeter-Wave Imaging," 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020, pp. 1-6, doi: 10.1109/RadarConf2043947.2020.9266412.
[3] Hem Regmi, Moh Sabbir Saadat, Sanjib Sur, and Srihari Nelakuditi. 2021. SquiggleMilli: Approximating SAR Imaging on Mobile Millimeter-Wave Devices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 3, Article 125 (Sept 2021), 26 pages. https://doi.org/10.1145/3478113.
Required background: Relevant MSc, Strong signal processing (statistical and digital) knowledge, proficiency with Python. knowledge of radar concepts and SAR or ISAR is a plus.
Type of work: 20% literature/theory, 60% modelling/simulation, 20% experimental
Supervisor: Hichem Sahli
Daily advisor: Hamed Javadi, Adnan Al baba
The reference code for this position is 2025-143. Mention this reference code on your application form.