Radar systems, which use radio waves to detect and track objects, play a crucial role in modern defense, aviation, and surveillance, yet their effectiveness is often challenged by environmental clutter, meaning unwanted signals from objects like buildings, trees, or the ground that interfere with radar detection. A team of researchers from Northwest University and Xi’an Institute of Space Radio Technology in China, led by Professor Cai Wen, has developed an innovative approach to improve radar moving target detection. Their study, published in the peer-reviewed journal Remote Sensing, introduces a novel detection network that uses an AI-aided learning approach that adapts quickly to new situations and a focus-enhancing method to improve detection.
Traditional radar systems struggle with detecting moving targets in complex environments due to strong and heterogeneous clutter echoes, which are reflections from non-target objects that make it harder to identify actual moving targets. This makes it difficult to distinguish weak signals from background noise. To address this issue, the research team proposed a detection network that first undergoes offline training using simulated radar data, reducing the need for extensive online training. A small amount of real-time data, meaning live, continuously updated information, is then used to fine-tune the network, ensuring adaptability to real-world conditions. “The use of small-sample transfer learning allows the system to quickly adjust to new clutter environments while maintaining high detection accuracy,” explained Professor Wen.
A key innovation in this study is the integration of an attention mechanism, a method that helps focus on the most important parts of the radar signal to improve detection within a specific radar data field that helps analyze movement patterns. This mechanism helps the network prioritize essential features, improving its ability to differentiate between moving targets and background clutter. The research team conducted extensive simulations to validate their approach, demonstrating that the attention mechanism significantly enhances clutter suppression, a technique to reduce interference from unwanted signals, even in situations where the target signals are very weak compared to background noise. “Our simulations show that the attention mechanism improves classification accuracy, the ability of the system to correctly identify targets, allowing the system to detect targets more effectively even in challenging scenarios,” said Professor Wen.
Compared to conventional methods, the proposed network achieves reduce the amount of processing power needed, which is the computing ability required to handle large amounts of data quickly while maintaining robust detection performance. Traditional space-time adaptive processing techniques require a large number of independent training samples, which are often unavailable in varied and unpredictable surroundings. The new approach reduces reliance on these samples, making real-time detection, the ability to identify moving targets instantly without delays more feasible for airborne and spaceborne radar systems.
The findings of this study pave the way for more efficient and reliable radar detection systems, with potential applications in defense, aviation, and remote sensing. By combining small-sample transfer learning with attention mechanisms, this approach offers a powerful alternative to existing detection methods. Future research may focus on further optimizing the network for real-world deployment and extending its capabilities to different radar platforms.
Journal Reference
Zhu J., Wen C., Duan C., Wang W., Yang X. “Radar Moving Target Detection Based on Small-Sample Transfer Learning and Attention Mechanism.” Remote Sens, 2024; 16: 4325. DOI: https://doi.org/10.3390/rs16224325
About the Author
Professor Cai Wen obtained his Bachelor’s degree from the School of Electronic Engineering at Xidian University in July 2009, and his Doctoral degree in Engineering from the National Key Laboratory of Radar Signal Processing at Xidian University in December 2014. From November 2019 to March 2023, he served as a Postdoctoral Research Fellow in the Department of Electrical and Computer Engineering at McMaster University in Canada. Since November 2016, he has been an Assistant Professor at the School of Information Science and Technology, Northwest University, and was promoted to Associate Professor in 2019 by exception.
He has led more than 10 national and provincial-level projects, including the National Natural Science Foundation of China, and several industrial projects. He has also participated in numerous research projects, such as the National Defense Pre-research Program, the National Basic Research Program (973 Program), and the National Key Research and Development Program. He has published over 80 SCI/EI-indexed papers in top international academic journals and conferences, including IEEE TSP, IEEE TAES and IEEE TGRS. Among these publications, five papers are highly cited by ESI, and three are IEEE Transactions hot papers. He has authored three academic monographs and holds more than 10 authorized invention patents.
Professor Cai Wen has served as a session chair and TPC member at several prestigious international conferences and has acted as a reviewer and team leader for multiple national projects. He currently serves as a Editorial Board member for the Journal of Naval Aeronautical and Astronautical University and Modern Radar. He is also a senior member of the Chinese Institute of Electronics and the China Radar Industry Association. He is a recipient of the Chinese “Postdoctoral International Exchange Program” and the “Young Academic Talent Support Program” at Northwest University. His research interests focus on Radar Signal Processing, Integrated Sensing and Communication (ISAC) and Artificial Intelligence (AI).
