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 in our office in 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.

Qualifications

Master of Science student with knowledge in C/C++ and machine learning as well as general image processing.