Error-Constrained Entropy-Minimizing Strategies for Multi-UAV Deception Against Networked Radars.

Area of Science:

• Electromagnetic environments
• Information theory
• Control systems

Background:

• Complex electromagnetic environments pose challenges for Unmanned Aerial Vehicle (UAV) swarm track deception due to spatial coupling uncertainties.
• Position errors and timing jitter increase false target information entropy, reducing the effectiveness of deception strategies.

Purpose of the Study:

• To propose an error-constrained entropy-minimizing compensation framework to model and mitigate radar/UAV errors and their spatial coupling.
• To enhance the robustness and reliability of UAV swarm track deception in complex electromagnetic environments.

Main Methods:

• Developed a framework establishing closed-form gate association conditions based on entropy minimization principles.
• Implemented two strategies: Zonal track compensation using auxiliary deception echoes and Formation jamming compensation adapting UAV swarm geometry.
• Ensured mutual consistency of false target measurements across multiple radars.

Main Results:

• The proposed framework reduces spatial inconsistency entropy by 50% compared to traditional methods.
• Demonstrated improved false target consistency and enhanced radar deception reliability.
• Information entropy bands and geometric symmetry effectively concentrated mutual information and suppressed position error diffusion.

Conclusions:

• The error-constrained entropy-minimizing compensation framework effectively addresses spatial coupling uncertainties in UAV swarm track deception.
• The proposed compensation strategies significantly improve the reliability and effectiveness of radar deception.
• This approach offers a robust solution for robust UAV swarm track deception in challenging electromagnetic environments.