IridiumRF-MR: A Multi-Receiver One-Dimensional I/Q Dataset for Specific Emitter Identification of LEO Satellites

Aiming at the lack of public dataset of satellite Specific Emitter Identification (SEI) and the problem of insufficient data under the condition of multiple receivers, this paper constructs a multi-receiver one-dimensional I/Q dataset for Iridium downlink signal through six software radio receivers with the same parameter configuration. The dataset contains 367,658 valid samples from 66 Iridium satellites. Each sample contains metadata such as SNR, acquisition timestamp, location information, and environmental information. At the same time, the validity of the dataset was evaluated by three experiments under seven deep learning models, different input lengths and different signal-to-noise ratios. The experimental results show that the dataset can effectively support the training and testing of different deep learning models, and can reflect the influence of signal length and signal quality on the performance of satellite individual identification. It provides data basis for satellite radio-frequency fingerprinting identification, low signal-to-noise ratio identification and model performance evaluation under the condition of multiple receivers.