Date of Graduation

2017

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Jason Gross

Committee Co-Chair

Patrick Browning

Committee Member

Marcello Napolitano

Abstract

To overcome the rapid and unbounded error growth of low-cost Inertial Navigation Systems (INS), aircraft localization methods commonly compensate for Inertial Measurement Unit (IMU) sensor errors by integrating them with Global Positioning System (GPS) measurements via a Kalman Filter. However, over the past decade, the potential of GPS jamming or even spoofing GPS signals has forced the research community to focus on the development of GPS-denied navigation technologies. Among the GPS-denied techniques, one approach that has been considered is the use of a Vehicle Dynamic Models (VDM) to reduce the rate at which an INS becomes unusable. As such, this Master's thesis considers the use of different aerodynamic modeling approaches to aid in compensation of IMU errors of a fixed-wing Unmanned Aerial Vehicle (UAV). The goals of this research are to evaluate the sensitivity of the performance of dynamic model aided navigation in the context of low-cost platforms where performance benefit must be weighed against the complexity that is required to develop the dynamic model. To do this, first, in simulation, the sensitivity to the required modeling accuracy is shown by perturbing the model coefficients with errors. In addition, different sensors and sensor grades are evaluated, and three different model-aided navigation architectures are discussed and evaluated. To conduct this work, a UAV simulation is developed within which a UAV trajectory is driven by ``truth'' dynamic model and then IMU measurements are derived and errors are added to them using standard stochastic models for IMU sensors. Finally, the algorithm performance is then evaluated using actual UAV flight testing data from a low cost testbed equipped with GPS and IMU sensors. The testbed used and modeled is a 2.4 m span fixed wing UAV designed and instrumented at WVU.

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