Development of a Peripheral-Central Vision System to Detect and Characterize Airborne Threats

With the rapid proliferation of small unmanned aircraft systems (UAS), the risk of mid-air collisions is growing, as is the risk associated with the malicious use of these systems. The airborne detect-and-avoid (ABDAA) problem and the counter-UAS problem have similar sensing requirements for detecting and tracking airborne threats. In this dissertation, two image-based sensing methods are merged to mimic human vision in support of counter-UAS applications. In the proposed sensing system architecture, a peripheral vision'' camera (with a fisheye lens) provides a large field-of-view while a central vision'' camera (with a perspective lens) provides high resolution imagery of a specific object. This pair form a heterogeneous stereo vision system that can support range resolution. A novel peripheral-central vision (PCV) system to detect, localize, and classify an airborne threat is first introduced. To improve the developed PCV system's capability, three novel algorithms for the PCV system are devised: a model-based path prediction algorithm for fixed-wing unmanned aircraft, a multiple threat scheduling algorithm considering not only the risk of threats but also the time required for observation, and the heterogeneous stereo-vision optimal placement (HSOP) algorithm providing optimal locations for multiple PCV systems to minimize the localization error of threat aircraft. The performance of algorithms is assessed using an experimental data set and simulations.