Iterative Tomographic Algorithm for Laser Radar Imaging of Unmanned-Aerial Vehicles

As unmanned-aerial vehicles (UAVs) become more prevalent, they represent a unique threat to deployed ground forces overseas and population centers in the US. The development of a robust imaging and surveillance capability targeting these aircraft is critical. A new tomographic reconstruction algorithm, Tomographic Reconstruction of Active Imaging Ladar Signals (TRAILS), is proposed for ground based laser radar imaging of unmanned-aerial vehicles (UAVs). TRAILS iteratively corrects for inaccurate UAV motion estimates and unavoidable limited-angle distortion. Limited-angle distortion is expected due to the short sweep angle over which the target motion is determined. The degree of limited-angle distortion is characterized for a UAV, including determination of observable features and specification of target trajectories for which the reconstruction is optimum. The proposed algorithm is benchmarked for real-time parallel implementation using a Beowulf cluster. The radar signal is simulated using a facet-based UAV QUick Image Display (QUID) model that includes the effects of oblique angle scattering and shadowing. Iterative reconstruction is applied to these higher order models to determine limited-angle distortion, motion solutions, and algorithm run times. The fidelity of the image reconstruction is verified by overlaying QUID wireframes projected into the image plane.