Zhenkai Yuan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Zhenkai Yuan has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Gaocai Li
Navigation college, Jimei University, Xiamen 361021, China
1 shared publication - Lei Cui
Navigation College, Jimei University, Xiamen 361001, China
1 shared publication - Mei Sun
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
1 shared publication - Yongfeng Suo
Navigation college, Jimei University, Xiamen 361021, China
1 shared publication
Latest Publications
- Open Access
- 31 July 2025
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 13 (8) , 1475
A Novel iTransformer-Based Approach for AIS Data-Assisted CFAR Detection
Detection of small vessels is of great significance for maritime safety assurance, abnormal vessel tracking, illegal fishing supervision, and combating smuggling. However, the radar reflection intensity of small vessels is low, making them difficult to detected with the radar’s constant false-alarm rate (CFAR) algorithm. To enhance the detection capability for small vessels, we propose an improved CFAR scheme. Specifically, we first compared traditional CFAR processing results of radar data with automatic identification system (AIS) data to identify some special targets. These special targets, which possessed AIS information, but remained undetected by radar, enabled an iTransformer model to generate more reasonable CFAR threshold adjustments. iTransformer adaptively lowered the threshold of the areas around these targets until they were detected by radar. This process made it easier to discover the small boats in the surrounding area. Experimental results showed that our method reduces the missed detection rate of small vessels by 73.4% and the false-alarm rate by 60.7% in simulated scenarios, significantly enhancing the CFAR detection capability. Overall, our study provides a new solution for ensuring maritime navigation safety and strengthening illegal supervision, while also offering new technical references for the field of radar detection.
- 31 July 2025
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 13 (8) , 1475
Open AccessA Novel iTransformer-Based Approach for AIS Data-Assisted CFAR Detection
Detection of small vessels is of great significance for maritime safety assurance, abnormal vessel tracking, illegal fishing supervision, and combating smuggling. However, the radar reflection intensity of small vessels is low, making them difficult to detected with the radar’s constant false-alarm rate (CFAR) algorithm. To enhance the detection capability for small vessels, we propose an improved CFAR scheme. Specifically, we first compared traditional CFAR processing results of radar data with automatic identification system (AIS) data to identify some special targets. These special targets, which possessed AIS information, but remained undetected by radar, enabled an iTransformer model to generate more reasonable CFAR threshold adjustments. iTransformer adaptively lowered the threshold of the areas around these targets until they were detected by radar. This process made it easier to discover the small boats in the surrounding area. Experimental results showed that our method reduces the missed detection rate of small vessels by 73.4% and the false-alarm rate by 60.7% in simulated scenarios, significantly enhancing the CFAR detection capability. Overall, our study provides a new solution for ensuring maritime navigation safety and strengthening illegal supervision, while also offering new technical references for the field of radar detection.