Chen Shang

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About

Chen Shang is a leading researcher in indoor positioning and mobile robotics, with a focus on overcoming signal interference in ultra-wideband (UWB) systems. His most-cited work, "Improved Extreme Learning Machine Based UWB Positioning for Mobile Robots with Signal Interference" (2022, 17 citations), addresses a critical challenge in autonomous navigation: maintaining accuracy when environmental noise disrupts UWB signals. Shang’s key contribution lies in integrating a genetic algorithm (GA) with an extreme learning machine (ELM) to create a binary classifier that distinguishes between clean and interfered signals, coupled with a compensation model that corrects positioning errors in real time. This hybrid approach significantly enhances the robustness of mobile robot localization in cluttered or dynamic indoor environments. By tackling the practical problem of signal degradation, Shang’s work bridges machine learning and sensor fusion, offering scalable solutions for logistics, warehouse automation, and smart infrastructure. His research continues to influence the development of resilient, low-cost positioning systems, making him a notable figure in the intersection of robotics and intelligent signal processing.