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Master theses

Current and past ideas and concepts for Master Theses.

360 degree perception of a vehicle surrounding using a network of radars


While multitarget tracking (MTT) is challenging in a single radar, a high performance is achievable by a network of multiple-input-multiple-output (MIMO) radars. Such multi-radar systems have become pervasive in self-driving applications for object detection and tracking purposes.
IMEC has successfully developed multi-radar MTT algorithms based on the state-of-the-art Probability Hypothesis Density?(PHD) filter [1] and the consensus-based track fusion [2]. The PHD filter is the state-of-the-art tracking algorithm which is based on the theory of random-finite-sets (RFS).
The goal of this master thesis is to further develop the MTT and fusion algorithms based on the cardinalized PHD (CPHD) filter [3]. In parallel, a data-driven deep-learning tracking model is developed [4,5]. For the end-to-end approach, the idea is to get insights from state-of-the-art deep learning models for camera- and lidar-based tracking and detection, and adapt those to fit in radar data-based detection and tracking tasks and compare these to the CPHD-based results.

Kind of work

  • Implementing the CPHD filter for MIMO radar
  • Track fusion to obtain 360o perception in the vehicle
  • Implementing an end-to-end deep learning approach for MIMOM radar detection and tracking.

    Master Thesis internship @ IMEC (6 months)
    Preceded by optional summer internship (max 3 months) @ IMEC

    Framework of the Thesis

    In collaboration with

    Dr. Seyed Hamed Javadi (IMEC)

    1. B.-N. Vo and W.-K. Ma, “The Gaussian Mixture Probability Hypothesis Density Filter,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4091–4104, 2006.
    2. G. Battistelli, L. Chisci, C. Fantacci, A. Farina and A. Graziano, "Consensus CPHD Filter for Distributed Multitarget Tracking," in IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 3, pp. 508-520, June 2013.
    3. B.-T. Vo, B.-N. Vo and A. Cantoni, “Analytic implementations of the cardinalized probability hypothesis density filter,” IEEE Trans. on Signal Processing, vol. 55, no. 7, pp. 3553-3567, 2007.
    4. A. Palffy, J. Dong, J. F. P. Kooij and D. M. Gavrila, "CNN Based Road User Detection Using the 3D Radar Cube," in?IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1263-1270, April 2020, doi: 10.1109/LRA.2020.2967272.
    5. J. F. Tilly?et al., "Detection and Tracking on Automotive Radar Data with Deep Learning,"?2020 IEEE 23rd International Conference on Information Fusion (FUSION), 2020, pp. 1-7.

    Expected Student Profile

  • Following an MSc in a field related to one or more of the following: Electrical engineering, Computer Science, or Applied Computer Science.
  • Experience with signal processing, and machine learning. Some knowledge of radar concepts is a plus.
  • Strong programming skills (Python).
  • Interest in developing state-of-the-art Machine Learning methods and conduct experiments.
  • Ability to write scientific reports and communicate research results at conferences in English.
  • Promotor

    Prof. Hichem Sahli

    +32 (0)2 629 2916

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