Tracking ground targets with measurements obtained from a single monocular camera mounted on an Unmanned Aerial Vehicle
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 also simulated based upon a fabricated environment consisting of features and a moving ground target. Target observability limitations are overcome by constraining the target vehicle to follow ground terrain, defined by local features, and subsequent modification of the target's observation model. An unscented Kalman filter (UKF) provides the simultaneous localization and mapping solution for the estimation of aircraft states and feature locations. Another filter, a loosely coupled Kalman filter for the target states, receives 3D measurements of target position with estimated covariance obtained by an unscented transformation (UT). The UT uses the mean and covariance from the camera measurements and from the UKF estimated aircraft states and feature locations to determine the estimated target mean and covariance. Simulation results confirm that the new loosely coupled filters are capable of estimating target states. Experimental data, collected from a research UAV, explores the effectiveness of the terrain estimation techniques required for target tracking.
School:Kansas State University
School Location:USA - Kansas
Source Type:Master's Thesis
Keywords:target tracking simultaneous localization and mapping unmanned aerial vehicle state estimation engineering mechanical 0548
Date of Publication:01/01/2007