CMOS and beyond CMOS
Discover why imec is the premier R&D center for advanced logic & memory devices. anced logic & memory devices.
Connected health solutions
Explore the technologies that will power tomorrow’s wearable, implantable, ingestible and non-contact devices.
Life sciences
See how imec brings the power of chip technology to the world of healthcare.
Sensor solutions for IoT
Dive into innovative solutions for sensor networks, high speed networks and sensor technologies.
Artificial intelligence
Explore the possibilities and technologies of AI.
More expertises
Discover all our expertises.
Be the first to reap the benefits of imec’s research by joining one of our programs or starting an exclusive bilateral collaboration.
Build on our expertise for the design, prototyping and low-volume manufacturing of your innovative nanotech components and products.
Use one of imec’s mature technologies for groundbreaking applications across a multitude of industries such as healthcare, agriculture and Industry 4.0.
Venturing and startups
Kick-start your business. Launch or expand your tech company by drawing on the funds and knowhow of imec’s ecosystem of tailored venturing support.
/Job opportunities/Target Tracking with MIMO Radar in the Presence of Multipath

Target Tracking with MIMO Radar in the Presence of Multipath

Research & development - Leuven | More than two weeks ago

You will track targets with a high resolution radar regardless of multipath ghosts

Multiple-input-multiple-output (MIMO) radars are widely used for target localization in many different fields such as airborne, remote-sensing, and autonomous driving systems. Recently,  they are also becoming popular in indoor environments for human target tracking and activity recognition. For both automotive and indoor MIMO radar, the complex urban/indoor environment leads to multipath reflections from static objects such as buildings and walls. The detections from the multipath are called ‘ghosts’, which generate objects in the radar detection list that, in fact, do not exist. In the state-of-the-art, the problem of removing multipath ghosts in MIMO radars remains unsolved. Even though there have been some solutions based on prior knowledge of the reflector position (geometry model), which are called ray-tracing techniques, to analyze or eliminate multipath, it is not a realistic assumption that this multipath geometry information is known a priori.

The goal of this master thesis is to develop algorithms to track targets with a MIMO radar in a multipath environment and jointly estimate the multipath reflectors (e.g. wall position and room boundary). In the previous research of our team, we have proposed algorithms to recognize multipath without prior knowledge of multipath geometry in an indoor environment. The master project will be based on the model and measurements that have already been achieved completed.

Probability Hypothesis Density (PHD) filter is the state-of-the-art tracking algorithm which is based on the theory of random-finite-sets (RFS). Compared to Bayesian filter based tracking approaches, such as the nearest neighbor standard filter (NNSF), joint probability data association filter (JPDAF) and multiple-hypothesis tracker (MHT), it can directly track multiple varying number of targets without any data association. The master project will use the PHD filter to solve the joint estimation problem. Both simulated data and data from measurements with a millimeter-wave radar will be used to validate the performance of the algorithms.

The task of the student will be to:

  1. study the basic MIMO radar signal processing
  2. understand the multipath model of MIMO radar
  3. study the theory of tracking, RFS and PHD filter
  4. simulate the joint target tracking and multipath estimation model for multi-targets and multi-walls with a PHD filter
  5. verify the algorithm by the real measurement.
  6. (depending on the ambition level) extend the multipath model to a moving platform.

The student should have basic knowledge of signal processing, detection theory, probability theory and statistics. The project will be implemented in MATLAB and Python.

For further information, please contact Ruoyu Feng ( and André Bourdoux (

Type of project: Combination of internship and thesis

Duration: 6 months

Required degree: Master of Engineering Science, Master of Engineering Technology

Required background: Electrotechnics/Electrical Engineering

Supervising scientist(s): For further information or for application, please contact: Ruoyu Feng (

Only for self-supporting students.