Dual-Radar Synthetic Dataset for Static Short-Range Object Detection

This dataset provides synthetic radar data generated using a raytracing-based simulation of a static short-range scenario. The simulated scene includes multiple objects in a semi-structured environment, such as a human and a forklift. Two radar sensors (Radar A and Radar B) observe the scene from different positions and angles, allowing for multi-perspective evaluation and sensor fusion analysis. The dataset includes: Radar range-Doppler data for Radar A and Radar B (stored in configA.h5 and configB.h5) Python scripts for dataset generation and processing (generate_radar_dataset.py, radar_utils.py) Ground truth object information in yolo format for detection tasks Configuration parameters for radar signal processing, antenna setup, interference modeling, and sensor placement The radar parameters in configB differ between Radar A and B in FFT size, resolution, and tilt angles, providing realistic variation for multi-sensor perception research. The dataset is suitable for training and evaluating radar-based object detection models (e.g., YOLO) and dual-radar fusion techniques.