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

RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding