Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Alfonso Sciacchitano, Douglas L. Van Bossuyt

Abstract

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.

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

Abstract

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.
Paper Structure (24 sections, 6 equations, 8 figures, 3 tables)

This paper contains 24 sections, 6 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Mission Engineering
  4. Comparison of Mission Engineering to Systems Engineering
  5. Mission Engineering Validation Methods
  6. Literature Review
  7. Mission Engineering Framework
  8. Mission Definition
  9. Design Space Parameterization
  10. Design Optimization
  11. Simulation
  12. Trade Space Analysis
  13. Mission Design Validation
  14. Methods and Case Study: Application of the Mission Engineering Framework
  15. Mission Definition
  16. ...and 9 more sections

Figures (8)

  • Figure 1: Mission engineering framework methodology. This high-level diagram depicts the automated process used in this study to determine the optimized system architecture based on mission requirements, constraints, and outcomes.
  • Figure 2: Concept of Operations for UAV-based MOB localization. The left UAV achieves successful localization because the MOB remains within its camera boresight, while the right UAV fails due to its boresight misalignment. This geometry highlights the critical role of sensor angle in determining both observability and the feasible trajectories UAVs can adopt to acquire valid bearing measurements and support mission success.
  • Figure 3: Pareto front obtained from optimizing UAV camera elevation angles between 0 and 90 degrees (NED coordinates) at a fixed 50 m altitude for the single-UAV configuration. The x-axis represents localization accuracy for the MOB while the y-axis shows the competing localization accuracy of the supporting vessel.
  • Figure 4: Pareto front obtained from optimizing UAV camera elevation angles between 0 and 90 degrees (NED coordinates) at fixed 50 m, 60 m and 70 m altitudes for a team of three UAVs, respectively. The x-axis represents localization accuracy for the MOB while the y-axis shows the competing localization accuracy of the supporting vessel.
  • Figure 5: Three-dimensional Pareto front showing the relationship between camera boresight angle, number of UAVs, and total mission cost. Each point represents a candidate design evaluated through Monte Carlo simulation, with red markers denoting the Pareto-optimal solutions.
  • ...and 3 more figures