Papers
1
Total Citations
24
H-Index
1
About
Pei He is a researcher focused on information fusion, sensor systems, and intelligent decision-making, with a particular emphasis on evidential reasoning approaches. His most-cited work, "Decision fusion of two sensors object classification based on the evidential reasoning rule" (2022), has garnered 24 citations, demonstrating its relevance in advancing multi-sensor data integration. He’s contributions lie in developing robust frameworks for combining heterogeneous sensor outputs, enhancing classification accuracy in uncertain environments—a critical need for autonomous systems and surveillance technologies. By applying the evidential reasoning rule, he addresses challenges in conflicting or incomplete data, offering a principled method for decision-level fusion. This work has implications for fields ranging from robotics to defense, where reliable object recognition is paramount. Pei He’s research bridges theoretical foundations and practical applications, making him a notable voice in the growing domain of intelligent sensor fusion. His ongoing efforts continue to refine how machines perceive and interpret complex, real-world scenarios.
Research Focus
Key Achievements
Top Papers
- 1