用虚拟轨迹提升多对多导弹导引效率，显著提高拦截成功率。
Many-vs-Many Missile Guidance via Virtual Targets
- 通过生成目标行为的虚拟轨迹，将多对多导引转为多对分布策略。
- 当拦截器多于目标时，拦截率提升5.8%至14.4%。
- 适合复杂对抗场景，尤其在数量占优时表现更优。
本文提出一种基于归一化流轨迹预测器生成虚拟目标（VT）的多对多导弹导引新方法。不同于传统武器-目标分配直接将n个拦截器分配给m个物理目标，本方法采用集中式策略，构建n条代表机动目标行为概率预测的虚拟轨迹。每个拦截器在中段飞行中使用零努力误差导引（Zero-Effort-Miss）向分配的虚拟目标逼近，末段切换为比例导航（Proportional Navigation）。该方法将多对多交战视为多对分布问题，利用数值优势（n > m），将拦截器分散至多个轨迹假设，而非追逐单一确定性预测。蒙特卡洛仿真显示，在不同配置（1-6个目标，1-8个拦截器）下，当n = m时，该方法性能与直线预测基线持平或提升0-4.1%；当n > m时，性能提升达5.8%-14.4%。结果表明，概率性虚拟目标能有效利用数量优势，显著提升多对多场景下的拦截概率。
原文摘要 · Abstract (English)
This paper presents a novel approach to many-vs-many missile guidance using virtual targets (VTs) generated by a Normalizing Flows-based trajectory predictor. Rather than assigning n interceptors directly to m physical targets through conventional weapon target assignment algorithms, we propose a centralized strategy that constructs n VT trajectories representing probabilistic predictions of maneuvering target behavior. Each interceptor is guided toward its assigned VT using Zero-Effort-Miss guidance during midcourse flight, transitioning to Proportional Navigation guidance for terminal interception. This approach treats many-vs-many engagements as many-vs-distribution scenarios, exploiting numerical superiority (n > m) by distributing interceptors across diverse trajectory hypotheses rather than pursuing identical deterministic predictions. Monte Carlo simulations across various target-interceptor configurations (1-6 targets, 1-8 interceptors) demonstrate that the VT method matches or exceeds baseline straight-line prediction performance by 0-4.1% when n = m, with improvements increasing to 5.8-14.4% when n > m. The results confirm that probabilistic VTs enable effective exploitation of numerical superiority, significantly increasing interception probability in many-vs-many scenarios.
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