发表机构
MIT Lincoln Laboratory（麻省理工学院林肯实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将微分博弈理论应用于无人机蜂群防御，通过对比基线战术，证实其能显著提升高价值资产的防御成功率，且统计可信度达99.9%。
AI 中文摘要
本技术报告研究了微分博弈（DG）理论在解决无人机蜂群的目标分配与中途制导问题中的应用，这些无人机蜂群的任务是拦截敌方蜂群以防御高价值资产。博弈论战术将敌方蜂群视为理性智能体，旨在寻求防御方与敌方之间的纳什均衡，研究人员将该战术与基线战术进行对比，基线战术将防御问题建模为防御方机动的单边优化。蒙特卡洛模拟与贝叶斯分析显示，博弈论方法成功拦截所有敌方的概率高于基线技术。当敌方蜂群具备规避机动能力时，防御成功概率的提升最为显著：与基线优化战术相比，微分博弈战术将估计的防御成功率从94.6%提升至96.8%，缩小了与完美防御之间剩余差距的约41%。为增强该结果的统计可信度，配对试验贝叶斯分析赋予99.9%的后验概率，表明在该场景下微分博弈战术的资产防御成功概率高于基线战术。
英文摘要
This technical report is a study of the use of differential game (DG) theory to solve the target-assignment and midcourse guidance problems of drone swarms tasked with intercepting opposing swarms in defense of high-value assets. The game-theoretic tactics---which treat the intruder swarm as a rational agent and seek a Nash equilibrium between defenders and intruders---are compared against baseline tactics that model the defense problem as a unilateral optimization of the defenders' maneuvers. Monte Carlo simulation and Bayesian analysis show that the game-theoretic approach has a higher probability of successfully intercepting all intruders than the baseline techniques. This improvement in successful defense probability is most pronounced when the intruder swarm is capable of evasive maneuvers: relative to baseline optimization tactics, differential-game tactics increase estimated defense success from 94.6% to 96.8%, closing approximately 41% of the remaining gap to perfect defense. To add statistical credibility to this result, a paired-trial Bayesian analysis assigns a 99.9% posterior probability that differential-game tactics have a higher probability of successful asset defense than baseline tactics in this scenario.
