A Game-Theoretic Decision Framework for Optimal Selection of Coordination Detection Methods in Multi-UAV Fleet Operations
2026-06-01 • Multiagent Systems
Multiagent SystemsComputer Science and Game Theory
AI summaryⓘ
The authors address the challenge of quickly and accurately detecting coordination among UAV fleets and identifying the lead aircraft guiding them. They propose a game-theoretic framework that treats choosing detection methods as a strategic game between a Monitor picking algorithms and Nature presenting different traffic scenarios. By analyzing multiple detection algorithms and their performance over many scenarios, the framework finds the best mix of methods to balance speed and accuracy depending on priorities. Their experiments show that different methods work best for different goals, providing a reliable way to adapt detection strategies in real-time UAV traffic management.
UAV fleetstrajectory surveillancegame theoryzero-sum gameMonte Carlo sensitivity analysismulti-objective optimizationPareto-optimalKoopman PhaseCRQAurban traffic management
Authors
Christian Manasseh
Abstract
Detecting coordination among unmanned aerial vehicle (UAV) fleets operating in shared airspace and identifying the route-lead aircraft whose navigation decisions govern fleet behavior presents a fundamental speed--accuracy trade-off: fast methods enable real-time traffic management but sacrifice detection fidelity, while accurate methods may exceed the time budget for actionable airspace deconfliction. This paper presents a game-theoretic decision framework that resolves this trade-off by formulating method selection as a two-player zero-sum game between a Monitor (selecting computational methods and parameters) and Nature (selecting the unknown traffic scenario). We construct an end-to-end pipeline from trajectory surveillance data through eight candidate detection algorithms, a Monte Carlo sensitivity analysis characterizing their stochastic performance, and finally a multi-objective optimization layer that identifies Pareto-optimal method portfolios. The minimax solution provides a robust mixed strategy with a probability distribution over methods that guarantees worst-case performance regardless of scenario uncertainty. Experimental evaluation across 200 randomized configurations spanning 5--50 aircraft demonstrates that the framework recommends distinct method portfolios depending on operational priority: Koopman Phase dominates balanced (70.6%) and speed-priority (79.7%) profiles, while CRQA emerges as primary (47.4%) when route-lead identification is prioritized. The framework achieves a guaranteed game value of 0.29--0.53 (normalized utility) across all tested preference profiles, providing the first principled, scenario-adaptive methodology for computational method selection in UTM fleet monitoring operations.