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Abstract

The core objective of this research is to develop an estimator capable of tracking the states of ground targets with observation measurements obtained from a single monocular camera mounted on a small unmanned aerial vehicle (UAV). Typical sensors on a small UAV include an inertial measurement unit (IMU) with three axes accelerometer and rate gyro sensors and a global positioning system (GPS) receiver which gives position and velocity estimates of the UAV. Camera images are combined with these measurements in state estimate filters to track ground features of opportunity and a target. The images are processed by a keypoint detection and matching algorithm that returns pixel coordinates for the features. Kinematic state equations are derived that reflect the relationships between the available input and output measurements and the states of the UAV, features, and target. These equations are used in the development of coupled state estimators for the dynamic state of the UAV, for estimation of feature positions, and for estimation of target position and velocity. The estimator developed is tested in MATLAB/SIMULINK, where GPS and IMU data are generated from the simulated states of a nonlinear model of a Navion aircraft. Images are als