Evaluation and Mitigation of Interference in Automotive Radar
Master’s thesis on interference-robust radar for autonomous vehicles
About Radar Reticence
Radar Reticence is a start-up that develops next-generation radar technology for safer and more
autonomous vehicles. Built on deep Swedish radar expertise and research from Halmstad
University, our patented software-based solution makes automotive radar more robust against
interference – one of the fastest-growing challenges in advanced driver assistance and autonomous
mobility.
We are now entering an exciting growth phase: moving from advanced research and validated testing
toward chip integration, customer proof-of-concepts and commercial deployment with leading
actors in the mobility and radar ecosystem. In 2026, Radar Reticence secured long-term financing
through a new investment round and was awarded EIC Accelerator funding from the European
Union.
This thesis work will be carried out at one of our offices in Halmstad or Linköping.
The Thesis
The project aims to investigate the performance of interference detection, localization, and mitigation
methods for automotive radar. Using one or more single-chip millimeter-wave radar evaluation
platforms, the students will experimentally evaluate the available interference-processing functions
under different interference scenarios and determine their strengths and limitations.
The project will also include a literature review of existing research on algorithms for detecting and
mitigating automotive radar interference. Based on the findings, promising algorithms may be selected,
implemented in the platform’s demonstration software, and evaluated experimentally. The objective is
to compare the performance of the investigated methods and determine which methods are best
suited to specific interference scenarios, as well as whether any method provides consistently strong
performance across a wide range of scenarios.
Qualifications
Master of Science student with knowledge in signal processing, as well as programming in C/C++ and
MATLAB or Python. Previous radar experience is a plus, not a requirement. Experience of laboratory
measurements and embedded platforms is an advantage.
The work can be done both as an individual project or as a joint project with two students.
Contact
Emil Nilsson, emil@radarreticence.com