Hongqi Fan

National University of Defense Technology

Papers

Total Citations

H-Index

About

Hongqi Fan is a leading researcher in autonomous perception and dynamic environment sensing, with a focus on high-speed target recognition and mobile robotics. His most cited work, "Perturbation Defense Ultra-High Speed Weak Target Recognition" (2024, 42 citations), introduces a novel framework for robustly identifying faint, fast-moving objects under adversarial conditions—a critical advancement for defense and autonomous navigation systems. Fan’s earlier foundational contribution, "A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment" (2017, 15 citations), addresses the challenge of robot perception in cluttered, populated spaces by fusing occupancy grid mapping with probability hypothesis density filtering, significantly improving state estimation and obstacle tracking. His more recent work, "Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform" (2024, 4 citations), further advances dynamic scene understanding by enabling efficient motion extraction from sequential grid maps, directly supporting Simultaneous Localization and Mapping (SLAM) and detection and tracking of moving objects (DATMO). Collectively, Fan’s research bridges theoretical filtering methods and practical robotic perception, offering scalable solutions for real-time environment monitoring. His contributions are shaping the next generation of autonomous systems operating in unpredictable, high-speed environments.