Intelligent Detection and Propagation Parameter Calculation of Medium-Scale Traveling Ionospheric Disturbances Based on YOLO and Feature Matching

Medium-scale traveling ionospheric disturbances (MSTIDs) are periodic wave-like structures in the ionosphere that can significantly alter the local ionospheric conditions leading to the performance degradation of the radio wave communication and satellite navigation. Due to their complex origins and evolving dynamics, traditional monitoring methods often fail to effectively extract the characteristic features of MSTIDs. Leveraging advances in deep learning, this study proposes the state-of-the-art MSTID intelligent recognition and propagation parameter inversion method based on the “You only look once” (YOLO) series models. The method consists of three main stages: MSTID target detection, MSTID blob instance segmentation, and interframe matching of segmented blobs. A dataset comprising 3422 annotated images for target detection and 236 images for instance segmentation was constructed using Differential Total Electron Content (DTEC) maps from Japan’s GEONET network under the different solar activity conditions. The method automatically extracts key propagation parameters from consecutive image pairs including velocity, azimuth, wavelength, period, and coverage area. Experimental results show that YOLO v9m achieves the highest detection accuracy (78.34%) for MSTID targets, while YOLO v8m-seg excels in instance segmentation (95.58%). All of the models satisfy the real-time processing requirements. Blob features, including color, centroid, area, Hu moments, and topology, are extracted to compute feature difference scores across consecutive frames for the optimal matches determined by the Hungarian algorithm. Ellipse fitting and pixel-to-geographic coordinate conversion are then employed to calculate propagation parameters. Comparative validation with the traditional three-station cross-spectral and keogram methods demonstrates good consistency, with errors in parameters such as propagation velocity and azimuth within 20%. This AI-based approach offers a promising solution for the advancing intelligent, accurate, and real-time ionospheric disturbance detection.