  Radar sensors employed for environment perception, e.g. in autonomous
vehicles, output a lot of unwanted clutter. These points, for which no
corresponding real objects exist, are a major source of errors in following
processing steps like object detection or tracking. We therefore present two
novel neural network setups for identifying clutter. The input data, network
architectures and training configuration are adjusted specifically for this
task. Special attention is paid to the downsampling of point clouds composed of
multiple sensor scans. In an extensive evaluation, the new setups display
substantially better performance than existing approaches. Because there is no
suitable public data set in which clutter is annotated, we design a method to
automatically generate the respective labels. By applying it to existing data
with object annotations and releasing its code, we effectively create the first
freely available radar clutter data set representing real-world driving
scenarios. Code and instructions are accessible at
www.github.com/kopp-j/clutter-ds.
