Classification of Dynamic Vulnerable Road Users Using a Polarimetric mm-Wave MIMO Radar
W. Bouwmeester (TU Delft - Microwave Sensing, Signals & Systems)
Francesco Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
A Yarovoy (TU Delft - Microwave Sensing, Signals & Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.