A noise analysis of 4D RADAR: robust sensing for automotive?
- Pak Hung Chan ,
- Sepeedeh Shahbeigi Roudposhti ,
- Xinyi Ye ,
- Valentina Donzella
Abstract
The sensor suite for assisted and automated driving functions vehicle is
critical to the function of a vehicle, but also the first and most
important limitation to the level of automation that the system can
achieve. The advancement of 4D RADARs, providing better resolution in
both azimuth and elevation compared to traditional RADAR, can assist to
achieve more robust situational awareness, whilst also providing more
data for perception algorithms and sensor fusion. However, like all
perception sensors, 4D RADAR is also affected by numerous noise factors.
To explore the sources of noise, this work identifies, classifies, and
analyses automotive 4D RADAR noise factors. Overall, 22 noise factors
have been considered, in combination with their effect on six 4D RADAR
outputs. Finally, this work also presents and applies, for the first
time, a dissimilarity metric to collected 4D RADAR data in the presence
of rain with different intensities. The proposed metric is used to
assess the effect of noise on the variability of the measured data, in
addition it can be used to compare any 4D RADAR data. The metric,
combined with other pointcloud evaluations, shows that as rainrate
intensifies, the size of the pointcloud decreases, but also the
variation in the measurements increases. This work presents the
importance of evaluating, companding, and quantifying noise for 4D
RADAR, and can pave the way for more in depth analysis of its modelling
and testing of 4D RADAR for assisted and automated driving functions.