Mo Song
Papers
1
Total Citations
8
H-Index
1
About
Mo Song is a researcher specializing in unmanned aerial vehicle (UAV) mission planning, with a focus on penetration route optimization and radar threat modeling. Their most-cited work, "The new environment model building method of penetration mission based on the artificial potential field approach" (2012, 8 citations), addresses a critical gap in UAV survivability by introducing a refined artificial potential field method that more accurately captures the detecting characteristics of radar and radar networks. This contribution enhances the realism of threat environment modeling, directly improving the success probability of penetration missions—a key challenge in modern aerial warfare and autonomous systems. While their citation count is modest, the work demonstrates foundational thinking in integrating potential field theory with radar detection dynamics, offering a practical tool for route planning under adversarial conditions. Mo Song’s research is particularly relevant for students and engineers working on UAV autonomy, path planning algorithms, and defense systems, providing a stepping stone for more sophisticated environment-aware navigation strategies.
Research Focus
Key Achievements
Top Papers
- 1