  Four-dimensional (4D) Radar is a useful sensor for 3D object detection and
the relative radial speed estimation of surrounding objects under various
weather conditions. However, since Radar measurements are corrupted with
invalid components such as noise, interference, and clutter, it is necessary to
employ a preprocessing algorithm before the 3D object detection with neural
networks. In this paper, we propose RTNH+ that is an enhanced version of RTNH,
a 4D Radar object detection network, by two novel algorithms. The first
algorithm is the combined constant false alarm rate (CFAR)-based two-level
preprocessing (CCTP) algorithm that generates two filtered measurements of
different characteristics using the same 4D Radar measurements, which can
enrich the information of the input to the 4D Radar object detection network.
The second is the vertical encoding (VE) algorithm that effectively encodes
vertical features of the road objects from the CCTP outputs. We provide details
of the RTNH+, and demonstrate that RTNH+ achieves significant performance
improvement of 10.14\% in ${{AP}_{3D}^{IoU=0.3}}$ and 16.12\% in
${{AP}_{3D}^{IoU=0.5}}$ over RTNH.
