Guilin Zhang

Shandong University of Science and Technology

Papers

Total Citations

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About

Dr. Guilin Zhang is a robotics researcher whose work centers on autonomous navigation and target tracking, with a particular focus on improving the precision and robustness of robotic systems in dynamic environments. His most notable contribution is the development of an improved line-of-sight (LOS) guidance law for tracking moving targets, detailed in his 2018 paper, which has garnered 11 citations. In this work, Zhang addressed a critical challenge in robotics—maintaining accurate tracking despite sensor noise—by integrating an optimal information fusion Kalman filter weighted by scalars. This innovation fuses data from two sensors, significantly enhancing trajectory tracking precision. By tackling the practical problem of sensor uncertainty, Zhang’s research has direct implications for applications in autonomous vehicles, drone surveillance, and industrial robotics, where reliable tracking of moving objects is essential. His work stands out for its elegant combination of guidance theory and sensor fusion, offering a scalable solution to a common real-world problem. For students and researchers in robotics and control systems, Zhang’s contributions provide a foundational approach to improving autonomous system performance under imperfect sensing conditions.