A Noise Analysis of 4D RADAR: Robust Sensing for Automotive?

The sensor suite for assisted and automated/ autonomous driving functions is critical to the function of a vehicle, but also the first and most significant limitation to the level of automation that the system can achieve. The advancement of 4D RADARs, offering 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 affected by numerous noise factors. To explore the sources of noise, this work identifies, classifies, and analyses automotive 4D RADAR noise factors. For the first time, 23 noise factors have been considered, in combination with their effect on six 4D RADAR outputs. Finally, this work also proposes and applies a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">novel dissimilarity metric</i> to collected 4D RADAR data in the presence of rain and snow 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 applied to compare any 4D RADAR data. The metric, combined with other pointcloud evaluations, shows that as rain rate or snow rate intensify, the size of the pointcloud changes, the factors in the measurements increases. This work presents the importance of evaluating, compounding, and quantifying noise for 4D RADAR, and can pave the way for more in depth quantitative analysis of modelling and testing of 4D RADAR for assisted and automated driving functions.