Date of Award


Document Type


Degree Name

Master of Science in Electrical Engineering


Department of Electrical and Computer Engineering

First Advisor

Michael J. Veth, PhD


Unmanned aerial vehicles are no longer used for just reconnaissance. Current requirements call for smaller autonomous vehicles that replace the human in high-risk activities. Many times these activities are performed in GPS-degraded environments. Without GPS providing today's most accurate navigation solution, autonomous navigation in tight areas is more difficult. Today, image-aided navigation is used and other methods are explored to more accurately navigate in such areas (e.g., indoors). This thesis explores the use of inertial measurements and navigation solution updates using cameras with a model-based Linear Quadratic Gaussian controller. To demonstrate the methods behind this research, the controller will provide inputs to a micro-sized helicopter that allows the vehicle to maintain hover. A new method for obtaining a more accurate navigation solution was devised, originating from the following basic setup. To begin, a nonlinear system model was identified for a micro-sized, commercial, off-the-shelf helicopter. This model was verified, then linearized about the hover condition to construct a Linear Quadratic Regulator (LQR). The state error estimates, provided by an Unscented Kalman Filter using simulated image measurement updates, are used to update the navigation solution provided by inertial measurement sensors using strapdown mechanization equations. The navigation solution is used with a reference signal to determine the position and heading error. This error, along with other states, is fed to the LQR, which controls the helicopter. Research revealed that by combining the navigation solution from the INS mechanization block with a model-based navigation solution, and combining the INS error model and system model during the time propagation in the UKF, the navigation solution error decreases by 20%. The equations used for this modification stem from state and covariance combination methods utilized in the Federated Kalman Filter.

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