Semester

Summer

Date of Graduation

2008

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Marcello R. Napolitano.

Abstract

Inertial Navigation Systems (INS) with the level of precision needed for Unmanned Aerial Vehicles (UAV) can easily cost more than the vehicle itself. This drastically increases the amount of aircraft power consumption and payload weight that drives the need for a low cost solution. This can be achieved through the use of sensor fusion techniques on low cost accelerometers and gyroscopes fused with Global Positioning System (GPS) data. In this paper, existing GPS and Inertial Measurement Unit (IMU) flight data is fused with the use of both an Kalman filter (KF) and Extended Kalman filter (EKF) methods for a more accurate estimate of the aircraft attitude, velocity, and position eliminating the need for the high cost attitude sensors. A simulation study shows that four sensor fusion methods verifying that an improvement of position, velocity, and attitude can be achieved using low-cost sensors. The first method incorporates a six state KF that corrects INS/GPS position and velocity errors. The second method features the GPS to estimate attitude parameters, which in turn uses in an EKF to correct INS attitude values. With this method, improved attitude values are obtained without the calculation of the full INS state; such that the INS position and velocity are not required, reducing the computational load. The third method uses only the GPS and INS position and velocity to correct for the errors in the full state of the INS also using an EKF. Finally, the last method combines the GPS attitude of the second method and the error reduction of the third method to further decrease the error in the velocity, position, and attitude of the system. The simulation results illustrate that all of the methods tested provide performance improvement to the system, and could be implemented in real-time on a UAV for accurate navigation parameters.

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