Satellite Payload Noncooperative Tactical Communication Signal Monitoring: Dataset and IoT Edge Computing Method

In satellite-based tactical communication systems, non-cooperative radio frequency (RF) communication signals are widely used, but their detection remains highly challenging in complex electromagnetic environments characterized by low signal-to-noise ratios, multi-signal coexistence, and dynamic interference. This paper focuses on the most widely used non-cooperative communication protocols—Link 11 and Link 4A—and proposes a cross-modal detection approach that maps signals into the image domain through high-resolution time-frequency analysis, enhancing detection robustness and interpretability. Additionally, we construct SC2SM, the first benchmark dataset specifically designed for communication signal detection, comprising 14,791 time-frequency images and over 85,000 annotated bounding boxes, comprehensively covering aliasing, strong interference, and other complex environmental scenarios. Furthermore, we introduce YOLO-Link, the first optimized object detection framework for this task, which enables efficient deployment on Internet of Things (IoT) edge computing platforms. Extensive experimental results demonstrate that, after training on the SC2SM dataset, YOLO-Link achieves state-of-the-art performance in communication signal detection, outperforming existing methods by 4.1% in Recall and achieving real-time inference at 40 FPS on IoT edge computing platforms, striking an optimal balance between detection accuracy and computational efficiency. This study provides technical support for intelligent detection and monitoring of non-cooperative communication signals and promotes the application of cross-modal learning in complex electromagnetic environments. The code and dataset are available at.