Toward a Data‑Driven Framework for Optimizing Atmospheric Sensor Placement on Multirotor sUAS
Presenter Abstract
11th Conference of the International Society for Atmospheric Research using Remotely-piloted Aircraft, 24–28 August 2026 in Daytona Beach, Florida, USA
Toward a Data‑Driven Framework for Optimizing Atmospheric Sensor Placement on Multirotor sUAS
Edgar E. Prada1*, Kevin A. Adkins2, Bryan Watson1, Christopher S. Cerqueira1,
Avinash Muthu Krishnan3
1 Electrical Engineering and Computer Science Department, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
2 College of Aviation, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
3Aeronautical Science Department, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
* Correspondence email: pradae@my.erau.edu
Multirotor small uncrewed aircraft systems (sUAS) have become essential tools for atmospheric boundary‑layer research, yet reliable in‑situ measurements remain degraded by rotor‑induced turbulence, pressure gradients, and structural vibration. Despite increasing operational use, no standardized, data‑driven methodology exist for determining optimal sensor placement on these platforms. Current approaches often rely on intuition, platform-specific experience, or computational fluid dynamics (CFD) models that are sensitive to assumptions and do not generalize across configurations.
This work presents a structured, data-driven framework for optimizing atmospheric sensor placement on multi-rotor sUAS. The approach integrates three elements: (1) SysML‑based system modeling to formally define sensing requirements, physical constraints, and verification pathways; (2) empirical characterization of rotor‑induced disturbances using 2D sonic anemometers, environmental sensors, and synchronized IMU/GPS measurements, with emphasis on repeatability and measurement uncertainty across flight conditions; and (3) multi‑objective design optimization, in which a Pareto frontier balances competing objectives such as minimizing turbulence exposure, measurement noise and bias.
The framework does not attempt deterministic prediction of turbulent flow fields. Instead, it models the statistical impact of rotor-induced disturbances on sensor performance as a function of sensor mounting location. Initial system architecture development and flight testing are expected to demonstrate consistent spatial trends in disturbance intensity and support the feasibility of constructing location-dependent performance models. The resulting sensor placement solutions are configuration-specific; however, the proposed workflow is transferable across multirotor platforms. The contribution of this work lies in generalizing the design process rather than the geometric solution, enabling a repeatable methodology for instrumenting sUAS platforms. This work introduces the framework, reports initial findings, and outlines a path toward a standardized, empirically grounded approach that better informs atmospheric sensor placement.
Presentations
Presented in Session 6: Sensors II
Toward a Data‑Driven Framework for Optimizing Atmospheric Sensor Placement on Multirotor sUAS
11th Conference of the International Society for Atmospheric Research using Remotely-piloted Aircraft, 24–28 August 2026 in Daytona Beach, Florida, USA
Toward a Data‑Driven Framework for Optimizing Atmospheric Sensor Placement on Multirotor sUAS
Edgar E. Prada1*, Kevin A. Adkins2, Bryan Watson1, Christopher S. Cerqueira1,
Avinash Muthu Krishnan3
1 Electrical Engineering and Computer Science Department, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
2 College of Aviation, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
3Aeronautical Science Department, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
* Correspondence email: pradae@my.erau.edu
Multirotor small uncrewed aircraft systems (sUAS) have become essential tools for atmospheric boundary‑layer research, yet reliable in‑situ measurements remain degraded by rotor‑induced turbulence, pressure gradients, and structural vibration. Despite increasing operational use, no standardized, data‑driven methodology exist for determining optimal sensor placement on these platforms. Current approaches often rely on intuition, platform-specific experience, or computational fluid dynamics (CFD) models that are sensitive to assumptions and do not generalize across configurations.
This work presents a structured, data-driven framework for optimizing atmospheric sensor placement on multi-rotor sUAS. The approach integrates three elements: (1) SysML‑based system modeling to formally define sensing requirements, physical constraints, and verification pathways; (2) empirical characterization of rotor‑induced disturbances using 2D sonic anemometers, environmental sensors, and synchronized IMU/GPS measurements, with emphasis on repeatability and measurement uncertainty across flight conditions; and (3) multi‑objective design optimization, in which a Pareto frontier balances competing objectives such as minimizing turbulence exposure, measurement noise and bias.
The framework does not attempt deterministic prediction of turbulent flow fields. Instead, it models the statistical impact of rotor-induced disturbances on sensor performance as a function of sensor mounting location. Initial system architecture development and flight testing are expected to demonstrate consistent spatial trends in disturbance intensity and support the feasibility of constructing location-dependent performance models. The resulting sensor placement solutions are configuration-specific; however, the proposed workflow is transferable across multirotor platforms. The contribution of this work lies in generalizing the design process rather than the geometric solution, enabling a repeatable methodology for instrumenting sUAS platforms. This work introduces the framework, reports initial findings, and outlines a path toward a standardized, empirically grounded approach that better informs atmospheric sensor placement.