Pengcheng Guo
Papers
1
Total Citations
8
H-Index
1
About
Pengcheng Guo is a researcher whose work lies at the intersection of sensor technology, uncertainty modeling, and indoor environmental perception. His primary research focus involves developing advanced detection and estimation models that leverage ultrasonic sensing to map and interpret physical spaces. Guo’s most notable contribution is the DSmT-Based Ultrasonic Detection Model for Estimating Indoor Environment Contour, which addresses critical challenges in ultrasonic sensor measurement—specifically, the inherent uncertainties in ranging and direction angle. By proposing a novel ultrasonic distance measurement model that systematically represents these uncertainties, Guo has provided a more reliable framework for detecting the contour of walls and obstacles in indoor settings. This work, cited 8 times, demonstrates his ability to fuse theoretical uncertainty reasoning with practical sensing applications. While his citation count is modest, the foundational nature of his research holds promise for advancements in robotics, autonomous navigation, and smart environment monitoring. Guo’s work is particularly valuable for students and researchers interested in sensor fusion, uncertainty quantification, and the practical deployment of low-cost sensing solutions in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1