Modeling and Simulation of High-Fidelity FMCW RADAR Sensor for Automated Trains

This paper focuses on developing a high-fidelity model of a frequency-modulated continuous wave (FMCW) radio detection and ranging (RADAR) sensor for automated train systems. The model uses ray tracing for virtual environmental perception in railway scenarios. It includes a multiple input multiple output (MIMO) antenna array and a complete signal processing toolchain of real RADAR sensors. The model outputs raw data, including range maps (RMs), range-Doppler maps (RDMs), and detection lists, including distance, relative radial velocity, and signal-to-noise ratio (SNR), radar-cross section (RCS), azimuth, and elevation angles. Results show a strong correlation with real measurements with a mean absolute percentage error (MAPE) below 4.8% for all the parameters defined at the detection level. To the author's knowledge, these error levels are among the lowest reported for RADAR sensor model validation. This finding allows for a cost-effective perception of virtual environments, facilitating simulation-based testing of automated railway systems.