A Mission Engineering Framework for Uncrewed Aerial Vehicle Design in GNSS-Denied Environments for Intelligence, Surveillance, and Reconnaissance Mission Sets
Small, low-size, weight, power, and cost (SWaP-C) uncrewed aerial vehicles (UAVs) are increasingly used for intelligence, surveillance, and reconnaissance (ISR) missions due to their affordability, attritability, and suitability for distributed operations. However, their design poses challenges including limited endurance, constrained payload capacity, and reliance on simple sensing modalities such as fixed-field-of-view, bearing-only cameras. Traditional platform-centric methods cannot capture the coupled performance, cost, and coordination trade-offs that emerge at the system-of-systems level. This paper presents a mission engineering framework for early-phase design of low-SWaP-C UAV ISR architectures. The framework integrates design of experiments, multi-objective optimization, and high-fidelity simulation into a closed-loop process linking design variables to estimator-informed performance and mission cost. Candidate architectures are explored via Latin hypercube sampling and refined using a genetic algorithm, with performance evaluated through Monte Carlo trials of a federated Kalman filter benchmarked against the posterior Cramer-Rao lower bound. Validation follows the Validation Square methodology, combining theoretical, empirical, and structural assessments. A case study on man-overboard localization in a GNSS-denied maritime environment shows that localization accuracy saturates at sub-meter levels, while higher-cost configurations primarily add redundancy and resilience. The framework thus quantifies mission trade-offs between performance, affordability, and robustness, providing a scalable decision-support tool for contested, resource-constrained ISR missions.