Papers
1
Total Citations
5
H-Index
1
About
Guang Han is a leading researcher in automotive radar systems and advanced signal processing, with a primary focus on enhancing the perception capabilities of intelligent vehicles. His most notable contribution is the development of a Local Resampling Fourier Transform for automotive FMCW radar, which dramatically improves range estimation accuracy in complex traffic environments. This work, published in 2016, addresses a critical challenge for driverless cars and driver-assistance systems: the need for precise target discrimination in cluttered, real-world conditions. By enabling more reliable distance measurements, Han’s method directly supports the safety and robustness of autonomous navigation and robotic perception. His research has garnered attention within the intelligent transportation community, with his key paper accumulating 5 citations as a foundational reference for subsequent radar signal processing studies. Han’s work bridges theoretical signal analysis and practical automotive applications, making him a valuable contributor to the ongoing evolution of driverless vehicle technology.
Research Focus
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Top Papers
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