Classification of Dynamic Vulnerable Road Users Using a Polarimetric mm-Wave MIMO Radar

Abstract

In this article, the classification of dynamic vulnerable road users
(VRUs) using polarimetric automotive radar is considered. To this end, a
signal processing pipeline for polarimetric automotive MIMO radar is
proposed, including a method to enhance angular resolution by combining
data from all polarimetric channels. The proposed signal processing
pipeline is applied to measurement data of three different types of VRUs
and a car, collected with a custom automotive polarimetric radar,
developed in collaboration with Huber+Suhner AG. Several polarimetric
features are estimated from the range-velocity signatures of the
measured targets and are subsequently analyzed. A Bayesian classifier
and a convolutional neural network (CNN) using these estimated
polarimetric features are proposed and their performance is compared
against their single-polarized counterparts. It is found that for the
Bayesian classifier, a significant increase in classification
performance is achieved, compared to the same classifier using single
polarized information. For the CNN-based classifier, utilizing the
distribution of polarimetric features of the target’s range-velocity
signatures also increases classification performance, compared to its
single-polarized version. This shows that polarimetric information is
valuable for classification of VRUs and objects of interest in
automotive radar.