AI让军事通信更智能,提升战场实时响应与抗干扰能力
AI-Driven Tactical Communications and Networking for Defense: A Survey and Emerging Trends
- 提出三维度评估框架,兼顾系统目标、通信约束与战场环境
- 实现雷达追踪、无人机中继、电子对抗等场景的自适应优化
- 适合关注军事AI、网络韧性及自主决策系统的研究人员
人工智能(AI)在军事通信与网络中的融合正重塑现代国防战略,提升安全数据交换、实时态势感知和自主决策能力。本文系统探讨了AI驱动技术在战术通信网络、基于雷达的数据传输、无人机辅助中继系统及电子战抗干扰能力方面的应用。重点分析了自适应信号处理、多智能体协同优化网络、雷达辅助目标跟踪以及AI驱动的电子对抗措施。提出一种新型三准则评估方法,从系统总体目标、军事通信约束和关键战术环境因素出发,对各类学习技术在多域网络互操作性与分布式信息融合中的应用进行评估。同时指出对抗性AI威胁、自主通信网络实时适应性不足以及当前模型在战场条件下的局限性等挑战。最后展望自愈网络、AI增强决策支持系统和智能频谱分配等新兴趋势,并提供未来研究的结构化路线图。
原文摘要 · Abstract (English)
The integration of Artificial Intelligence (AI) in military communications and networking is reshaping modern defense strategies, enhancing secure data exchange, real-time situational awareness, and autonomous decision-making. This survey explores how AI-driven technologies improve tactical communication networks, radar-based data transmission, UAV-assisted relay systems, and electronic warfare resilience. The study highlights AI applications in adaptive signal processing, multi-agent coordination for network optimization, radar-assisted target tracking, and AI-driven electronic countermeasures. Our work introduces a novel three-criteria evaluation methodology. It systematically assesses AI applications based on general system objectives, communications constraints in the military domain, and critical tactical environmental factors. We analyze key AI techniques for different types of learning applied to multi-domain network interoperability and distributed data information fusion in military operations. We also address challenges such as adversarial AI threats, the real-time adaptability of autonomous communication networks, and the limitations of current AI models under battlefield conditions. Finally, we discuss emerging trends in self-healing networks, AI-augmented decision support systems, and intelligent spectrum allocation. We provide a structured roadmap for future AI-driven defense communications and networking research.
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