Papers
5
Total Citations
60
H-Index
4
About
Yuxuan Xia is a rising researcher at the intersection of autonomous systems, robotics, and deep learning, whose work spans both foundational theory and practical hardware. His primary contributions lie in **multitarget tracking (MTT)** and **surgical robotics**, where he addresses the challenge of estimating unknown numbers of moving objects from noisy sensor data. In his most-cited work, "Next Generation Multitarget Trackers," Xia compares classical random finite set methods with transformer-based deep learning, establishing a critical benchmark for the field (24 citations). He has also advanced **exoskeleton control** through a CNN-BiLSTM compound network for gait phase classification (21 citations), enabling more responsive assistive devices. In surgical robotics, Xia developed novel **intravascular catheter bending recognition** and **multi-data detection** methods, tackling the critical lack of force feedback in robot-assisted interventional surgery—a risk that can harm patients. His work on fusing multi-object densities using transformers (4 citations) further demonstrates his push to integrate deep learning with probabilistic tracking. With over 60 total citations and a portfolio that bridges theory and application, Xia is shaping the next generation of intelligent, safe autonomous and robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4An Interventional Surgical Robot Based on Multi-Data Detection4 citations · 2023
- 5Deep Fusion of Multi-Object Densities Using Transformer4 citations · 2023