Abstract

Research in Unmanned Air Vehicles (UAV's) continues to push the limitations of size and weight. As technical advances have made UAV's smaller and less expensive, they have become more flexible and extensive in their roles. To continue using smaller and less expensive components while retaining and even enhancing performance requires more sophisticated processing of sensor data in order for the UAV to accurately determine its state and thereby allow the use of feedback in controlling the aircraft automatically. This work presents a three-stage state-estimation scheme for the class of UAV's know as Miniature Air Vehicles (MAV's). The first stage estimates pitch and roll, the second stage estimates heading, and the third stage produces a position estimate and an estimate of wind speed and direction. All three stages make use of the extended Kalman filter, a framework for using a system dynamic model to predict future states and to update the predictions using weighted sensor measurements as they become available, where the weighting is based on the relative uncertainty of the dynamic model and the sensors. Using the three-stage state esti-mation scheme, significant improvements in the estimation of pitch, roll and heading have been achieved in simulation and flight testing. Performance of the navigation (position and wind) stage is comparable to an existing baseline algorithms for position and wind, and shows additional promise for use in dead reckoning when GPS updates become unavailable.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2006-08-02

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd1527

Keywords

UAV MAV unmanned autonomous vehicle state estimation gyro gyroscope accelerometer pressure airspeed magnetometer compass GPS navigation wind kalman filter

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