Best Researcher Award
Ling Zhang
Ling Zhang is a researcher affiliated with Ocean University of China whose scholarly work integrates artificial intelligence, radar signal processing, maritime surveillance, and autonomous marine systems. Her publication portfolio demonstrates contributions to high-frequency surface wave radar technologies, target detection, and intelligent ocean engineering applications.[1]
Abstract
Ling Zhang has developed a research portfolio focused on artificial intelligence applications in maritime sensing, radar target detection, signal processing, and autonomous vessel technologies. Her work addresses challenges associated with shipborne high-frequency surface wave radar systems, clutter suppression, motion compensation, direction finding, and intelligent detection frameworks. Through publications in leading engineering and remote sensing journals, she has contributed methodologies that combine machine learning, deep feature fusion, and advanced radar analytics. These studies support improved situational awareness, marine monitoring, and autonomous ocean operations while advancing interdisciplinary collaboration between artificial intelligence and marine engineering research.[2]
Keywords
Artificial Intelligence, HFSWR, Radar Signal Processing, Target Detection, Marine Engineering, Autonomous Vessels.
Introduction
The integration of artificial intelligence into ocean observation and radar systems has become increasingly important for maritime safety and environmental monitoring. Ling Zhang’s research aligns with these developments through investigations into intelligent sensing technologies and data-driven detection methods.[3]
Research Profile
Her research profile encompasses radar engineering, machine learning, remote sensing, ocean engineering, and autonomous navigation systems. Published studies demonstrate continuous engagement with marine surveillance and intelligent maritime technologies.[2]
Research Contributions
Key contributions include deep feature fusion for radar target detection, direction-finding correction techniques, clutter suppression frameworks, and AI-enhanced path-planning algorithms for unmanned surface vessels. These studies strengthen the accuracy and operational effectiveness of maritime monitoring systems.[4]
Publications
Selected publications appear in IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, Ocean Engineering, IEEE Access, and Engineering Applications of Artificial Intelligence, reflecting interdisciplinary research activity and international visibility.[5]
Research Impact
With 56 indexed documents, 537 citations, and an h-index of 13, Ling Zhang’s work demonstrates measurable academic influence and engagement within radar technology, marine engineering, and artificial intelligence research communities.
Award Suitability
The combination of sustained publication activity, interdisciplinary innovation, and contributions to intelligent maritime technologies supports consideration for recognition through the Scientific World Research Awards program.
Conclusion
Ling Zhang’s research reflects ongoing efforts to advance artificial intelligence-enabled radar systems and marine technologies. Her scholarly output contributes to improved sensing, detection, and autonomous operational capabilities within maritime environments.
External Links
References
- Elsevier. (n.d.). Scopus author details: Ling Zhang, Author ID 57851292900. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57851292900
- ORCID. (2026). Ling Zhang ORCID Record.
https://orcid.org/0000-0002-1679-7128
- Wang, C., Zhang, L., et al. (2023). Accurate Direction Finding for Shipborne HFSWR Through Platform Motion Compensation.
https://doi.org/10.1109/TGRS.2023.3328264
- Wu, T., Zhang, L., et al. (2025). Two-Stage Target Detection for Compact HFSWR With Space-to-Depth YOLOv8 and Multiframe ViT.
DOI:10.1109/JSTARS.2025.3556138
- Lu, Y., Li, G., Zhang, L., et al. (2026). Orthogonal Momentum Progressive Subnetwork Representation Learning with Feature Fusion for Surface Wave Radar Target Detection.
https://doi.org/10.1016/j.engappai.2026.114821