A Mission Engineering Framework for Uncrewed Aerial Vehicle Design in GNSS-Denied Environments for Intelligence, Surveillance, and Reconnaissance Mission Sets

Researchers Alfonso Sciacchitano and Douglas L. Van Bossuyt have published a new paper outlining a comprehensive mission engineering framework for designing fleets of small, low-SWaP-C (Size, Weight, Power, and Cost) uncrewed aerial vehicles (UAVs) specifically for Intelligence, Surveillance, and Reconnaissance (ISR) missions in GNSS-denied environments. The work addresses a critical gap: traditional, platform-centric design methods fail to capture the complex system-of-systems trade-offs in performance, cost, and coordination that emerge when deploying multiple drones in contested areas where GPS signals are unreliable or jammed. The proposed framework moves beyond single-drone optimization to architect entire resilient, distributed systems.

The framework integrates design of experiments, multi-objective optimization, and high-fidelity simulation into a closed-loop process. It explores candidate drone fleet architectures using Latin hypercube sampling, then refines them with a genetic algorithm. Performance is rigorously evaluated through thousands of Monte Carlo simulation trials, using a federated Kalman filter benchmarked against theoretical limits like the posterior Cramer-Rao lower bound. In a case study on maritime man-overboard localization, the tool revealed that sub-meter accuracy is achievable, and that added cost primarily buys redundancy and resilience, not raw precision. This provides defense and security planners with a scalable, quantitative tool to make informed trade-offs between mission success, affordability, and robustness for next-generation ISR operations.