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.

提出了两种新算法的RTNH+是增强版4D雷达物体检测网络，通过预处理算法增强了输入信息，并通过垂直编码算法有效地编码了路面物体的垂直特征，实现了10.14%的平均精确度(AP)在IoU=0.3和16.12%的AP在IoU=0.5上与RTNH相比的显著性能改进。

RTNH+：使用组合的基于CFAR的双层预处理和垂直编码的增强型4D雷达目标检测网络