Papers
1
Total Citations
3
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About
Yubin Xu is a researcher specializing in multi-sensor information fusion, fractional-order systems, and Kalman filtering techniques, with a particular focus on asynchronous and multi-rate data integration in robotic systems. Their most notable contribution is the development of a fractional Kalman filter-based algorithm for asynchronous multi-rate sensor fusion, which addresses the challenge of integrating measurements from sensors operating at different rates with inherent delays. This work, published in 2018, introduces fractional calculus into the Kalman filtering framework to enhance the approximation accuracy of system states, offering a more robust solution for real-time robotic state estimation. Although the paper has garnered 3 citations to date, it represents a foundational step in bridging fractional-order theory with practical sensor fusion problems. Xu’s research is particularly relevant for applications in robotics, autonomous navigation, and control systems where heterogeneous sensors must be synchronized despite timing mismatches. Their work demonstrates a commitment to advancing estimation theory for complex, real-world systems, making it a valuable reference for students and researchers exploring advanced filtering methods in multi-sensor environments.
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