Research Area: Control and Measurement of Optoelectronic Tracking Systems. Addressing the core requirements of key engineering fields such as space exploration, laser communication, and precision space measurement, this research focuses on overcoming technical bottlenecks in high-precision acquisition, tracking, and measurement of dynamic targets. A multidisciplinary collaborative research framework has been established, with the following core research directions:

1. Space Precision Tracking and Stabilization Control Mechanism

This research direction focuses on advanced aerospace equipment such as space telescopes, deep-space exploration payloads, and space laser communication terminals. It aims to address the challenges of high-precision tracking and line-of-sight pointing under complex conditions—including microgravity environments, temperature gradients, and mechanical clearance disturbances. The study centers on modeling complex nonlinear systems based on multi-physics field coupling theory, developing nonlinear control strategies that balance robustness and dynamic response (such as sliding mode control and adaptive robust control), and integrating intelligent control methods incorporating operational condition awareness (including fuzzy adaptive control and reinforcement learning-based control). The goal is to achieve line-of-sight stabilization accuracy at the arcsecond to sub-arcsecond level, providing theoretical and technological support for high-precision pointing control in major space missions. Please provide the text you would like to be translated.

2. Sensor Information Fusion Technology

To meet the engineering requirements of multi-source sensor collaborative perception, this research focuses on the theoretical and methodological development of intelligent fusion for sensors with distinct characteristics, such as star trackers, gyroscopes, image sensors, encoders, and distributed MEMS sensors. Key research areas include spatiotemporal alignment of multi-source sensor data, robust outlier detection and removal, dynamic adaptive weight allocation, and the development of fusion algorithms based on federated Kalman filtering, evidence theory, and deep learning. The goal is to enhance the robustness and accuracy of system state estimation, providing precise state feedback and informational support for electro-optical tracking systems. Please provide the text you would like to be translated.

III. Multi-Agent Control and Intelligent Decision-Making

To address complex scenarios such as distributed multi-optoelectronic sensor collaborative monitoring and dynamic multi-target tracking, this research focuses on key technologies for intelligent deployment and coordinated control. It emphasizes the development of sensor optimization models that integrate environmental perception with mission requirements, consensus control strategies for multi-agent systems under dynamic network topologies (e.g., distributed model predictive control), and intelligent decision-making algorithms that incorporate task priorities and resource constraints (e.g., reinforcement learning-driven task allocation and conflict resolution). The goal is to achieve efficient collaboration among multi-agent systems, maximize overall detection and tracking performance, and provide theoretical and technical solutions for optoelectronic system coordination in complex environments.

   

2024-12 - Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences Associate Researcher

2020-07 - 2024-12 Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences Assistant Researcher

2015-09 - 2020-06 University of Chinese Academy of Sciences Doctor

2011-09 - 2015-06 Chang'an University Bachelor's Degree