Many-vs-Many Missile Guidance via Virtual Targets

TL;DR

This paper introduces a novel many-vs-many missile guidance method using virtual targets generated by a Normalizing Flows-based predictor, enabling better exploitation of numerical superiority in complex engagements.

Contribution

It proposes a centralized strategy that constructs probabilistic virtual target trajectories, improving interception success over traditional deterministic approaches.

Findings

VT method matches or exceeds baseline performance when n = m

Performance improves by 5.8-14.4% when n > m

Monte Carlo simulations validate effectiveness across configurations

Abstract

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.