Papers
2
Total Citations
10
H-Index
2
About
Hongmin Mao is a researcher specializing in sensor-based environmental perception and uncertainty modeling, with a particular focus on ultrasonic detection technologies for indoor spatial mapping. His primary research areas include multi-sensor data fusion, evidence theory (particularly Dezert-Smarandache Theory, or DSmT), and uncertainty representation in measurement systems. Mao’s major contribution lies in developing DSmT-based ultrasonic detection models that address the inherent uncertainties—such as distance and angle errors—in ultrasonic sensor measurements. His 2019 paper, "DSmT-Based Ultrasonic Detection Model for Estimating Indoor Environment Contour," which has garnered 8 citations, proposes a novel model to represent these uncertainties and accurately estimate indoor contours. This work builds on his earlier 2016 study, which laid the foundation for uncertainty representation in ultrasonic distance measurement. By integrating DSmT, a powerful framework for handling conflicting and uncertain information, Mao has advanced the reliability of indoor environment detection, offering practical solutions for robotics, autonomous navigation, and smart building applications. His research is particularly notable for bridging theoretical uncertainty modeling with real-world sensor challenges, making his work valuable for engineers and researchers in sensor fusion and environmental perception.
Research Focus
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Top Papers
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