Papers
2
Total Citations
5
H-Index
2
About
Weicong Zhan’s research lies at the intersection of autonomous marine robotics, sensor network optimization, and underwater target tracking. His work focuses on enabling fleets of autonomous underwater vehicles (AUVs) to operate as distributed, mobile, and cooperative sensor networks for environmental monitoring and surveillance. In his highly cited 2022 paper, Zhan developed methods to optimize the sensing locations of AUVs to improve both environmental prediction and acoustic target tracking, addressing the critical challenge of spatial coverage in dynamic underwater environments. His second major contribution introduced an Interacting Multiple Model (IMM) combined with a Bayesian propagation algorithm for tracking maneuvering underwater targets using multistatic active sonar networks formed by marine robots. This work directly supports applications in antisubmarine warfare and underwater security. Though early in his career, Zhan’s papers have already garnered citations from researchers in robotics, ocean engineering, and defense, signaling the practical relevance of his contributions. His research is particularly notable for bridging theoretical sensor placement optimization with real-world constraints of autonomous underwater systems, making his work essential reading for students and engineers developing next-generation marine surveillance technologies.
Research Focus
Key Achievements
Top Papers
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