Advanced driver assistance systems (ADAS) are transforming cars into securely-connected, highly-autonomous vehicles sensing the environment and making adequate decisions in a huge number of traffic conditions.

Key components are radar sensors measuring the range, angle, and velocity of objects around the vehicle. The sensor output is used to characterize the environment, and detect and classify vulnerable road users and vehicles. Limited physical aperture size of mm-wave radars results in poor angular resolution and very few detection points per volume box associated with a target. This makes classification of pedestrians and by cyclists a challenging task.

To solve this challenge, we propose to enrich the available information about a target by using polarimetric properties of electromagnetic waves. Solutions for three critical radar antenna system and signal processing challenges will be pursued: multiple input multiple output (MIMO) antenna array topology to support polametric information extraction, polarimetric antenna elements and array, calibration procedure for MIMO array for arbitrary positioned in azimuth targets.

The work in this project will be executed by a PhD’er. The research is crucial for the next steps in improving classification capabilities of automotive radar sensors and reliability of ADAS.