Multi-sensor dynamic scheduling for defending UAV swarms with Fresnel zone under complex terrain

Area of Science:

• Defense technology
• Robotics and autonomous systems
• Sensor networks

Background:

• Modern warfare increasingly involves unmanned aerial vehicle (UAV) swarms, posing significant challenges to existing defense systems.
• Complex terrain and limitations of multi-sensor resources (radar, photoelectric) complicate effective threat detection and engagement.
• Real-time decision-making is crucial for ground and air defense against dynamic UAV threats.

Purpose of the Study:

• To propose a novel multi-sensor dynamic scheduling algorithm for enhanced defense against UAV swarms.
• To address the complexities of terrain and sensor constraints in developing a robust detection model.
• To optimize sensor resource allocation for improved real-time threat response.

Main Methods:

• Established transmission models considering Fresnel zones in complex terrain and sensor models for radar/photoelectric systems.
• Developed a detection model incorporating constraints from pitch angle, array scanning angle, and threat levels.
• Implemented a fast Fresnel zone clearance calculation using an adaptive buffer and an improved Hungarian algorithm for sensor scheduling optimization.

Main Results:

• The proposed algorithm significantly reduces the sensor switching rate.
• Achieved high sensor-UAV matching rates and high-threat matching rates.
• Demonstrated effectiveness in simulation experiments under various conditions.

Conclusions:

• The developed multi-sensor dynamic scheduling algorithm provides an effective solution for defending against UAV swarms.
• The approach enhances real-time defense capabilities by optimizing sensor utilization.
• Validated effectiveness in complex environments and under diverse operational scenarios.