Factor Graph-Based Time-Synchronized Trajectory Planning for UAVs in Ground Radar Environment Simulation.

Area of Science:

• Robotics and Aerospace Engineering
• Radar Systems Engineering
• Signal Processing

Background:

• Unmanned aerial vehicles (UAVs) are increasingly used as mobile sensor platforms.
• Applications include emulating radar targets and dynamic measurement systems.
• Existing methods lack robust time synchronization for trajectory planning.

Purpose of the Study:

• To develop a novel, time-synchronized UAV trajectory planning method for radar environment simulation.
• To enable UAVs to accurately replicate the viewing geometry and motion of reference targets for ground-based radar.
• To explore the application of factor graphs for joint optimization of trajectory, geometry, and timing.

Main Methods:

• Formulation of the trajectory planning problem within a factor graph framework.
• Joint optimization of UAV spatial trajectory, observation angles, and temporal synchronization.
• Utilizing a software-defined radio (SDR) on the UAV to reproduce radar signatures.

Main Results:

• A unified optimization approach for space and time constraints in UAV trajectory planning.
• Demonstration of UAVs accurately reproducing target motion as perceived by radar.
• Successful emulation of complex aerial platform signatures and viewing geometries.

Conclusions:

• The proposed factor graph approach offers a powerful tool for time-synchronized UAV trajectory planning.
• This method significantly enhances the fidelity of radar environment simulation using UAVs.
• Future work can extend this framework to more complex multi-UAV and multi-radar scenarios.