In recent years, miniature radar-on-chip sensors have been explored for HCI by both academia and industry. This is driven by the availability of affordable radar hardware and advances in signal processing and machine learning. However, comparative evaluation of radar-based gesture interaction systems is still challenging. One possible dimension for comparison lies in the radar signal representations derived from raw voltage data. These representations commonly include range-Doppler, range-azimuth-angle, range-elevation-angle, point cloud and IQ radar cube formats. However, existing studies often restrict comparative analysis to a single signal representation type, typically focusing on gesture recognition algorithms or minor variations within Digital Signal Processing (DSP) pipelines. In this chapter, we address this through the development of an open-source application designed to facilitate efficient and reliable dataset preparation for comparative testing across different radar signal representationsRadar signal representations. The application supports visualization of various signal formats and includes a command-line interface for batch processing, thereby streamlining the dataset preparation workflow.