Yu Dan

Papers

Total Citations

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About

Yu Dan’s research centers on real-time vision systems and precision tracking for robotic applications, with a particular focus on ping-pong robots. Her major contributions lie in developing advanced Kalman filter-based algorithms that overcome significant tracking challenges, such as motion blur, camera image distortion, and aerodynamic uncertainties. In her 2009 work, she proposed an adapted measurement covariance digital Kalman filter method that dynamically adjusts to eliminate locating noises, enabling precise target motion trajectory tracking. She further refined this approach by incorporating an air drag factor estimation into the tracking algorithm, addressing errors caused by air resistance and other uncertain factors. Each of these foundational papers has garnered 4 citations, reflecting their niche but valuable impact on robotics and computer vision. Yu Dan’s work demonstrates a keen ability to integrate theoretical filtering techniques with practical robotic challenges, offering solutions that enhance real-time accuracy in dynamic environments. Her research is particularly notable for its application in high-speed, precision-demanding scenarios, making her contributions relevant for students and researchers exploring sensor fusion, adaptive control, or sports robotics.