Weixiang Zeng
Papers
1
Total Citations
7
H-Index
1
About
Weixiang Zeng is a researcher advancing the frontiers of autonomous underwater vehicle (AUV) technology, with a primary focus on visual tracking, anti-interference navigation, and intelligent recycling systems. His most cited work, "A Method for Long-Term Target Anti-Interference Tracking Combining Deep Learning and CKF for LARS Tracking and Capturing" (2023, 7 citations), tackles a critical bottleneck in marine robotics: reliably recovering AUVs in turbulent, visually degraded underwater environments. By fusing deep learning-based object detection with a Cubature Kalman Filter (CKF), Zeng’s approach dramatically improves tracking robustness against occlusion, lighting shifts, and sea-state disturbances—challenges that have long stymied autonomous marine operations. This contribution is pivotal for extending AUV endurance and enabling persistent ocean monitoring, resource exploration, and infrastructure inspection. While his citation count is still growing, the novelty of his method—bridging neural networks with classical state estimation for real-time, long-duration tracking—marks him as an emerging voice in underwater cyber-physical systems. Zeng’s work directly informs the design of next-generation launch and recovery systems, promising safer, more efficient AUV operations in the harshest marine conditions.
Research Focus
Key Achievements
Top Papers
- 1