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.

翻译：本文提出了一种利用基于归一化流的轨迹预测器生成虚拟目标（VT）的多对多导弹制导新方法。与传统通过武器目标分配算法直接将n枚拦截弹分配给m个物理目标不同，我们提出一种集中式策略，构建n条代表机动目标行为概率预测的VT轨迹。每枚拦截弹在中段飞行期间采用零控脱靶量制导向其分配的VT飞行，并在末端拦截阶段切换为比例导引制导。该方法将多对多交战视为多对分布场景，通过将拦截弹分散至多样化的轨迹假设而非追求相同的确定性预测，从而利用数量优势（n > m）。针对不同目标-拦截弹配置（1-6个目标，1-8枚拦截弹）的蒙特卡洛仿真表明：当n = m时，VT方法的性能与基线直线预测方法相当或超出0-4.1%；当n > m时，性能提升可达5.8-14.4%。结果证实概率性VT能有效利用数量优势，显著提升多对多场景下的拦截概率。