Development of a Peripheral–Central Vision System for Small Unmanned Aircraft Tracking

Abstract

Two image-based sensing methods are merged to mimic human vision in support of airborne detect-and-avoid and counter–unmanned aircraft systems applications. In the proposed sensing system architecture, a peripheral vision camera (with a fisheye lens) provides a large field of view, whereas a central vision camera (with a perspective lens) provides high-resolution imagery of a specific target. Beyond the complementary ability of the two cameras and supporting algorithms to enable passive detection and classification, the pair forms a heterogeneous stereo vision system that can support range resolution. The paper describes development and testing of a novel peripheral–central vision system to detect, localize, and classify an airborne threat. The system was used to generate a dataset for various types of mock threats in order to experimentally validate parametric analysis of the threat localization error. A system performance analysis based on Monte Carlo simulations is also described, providing further insight concerning the effect of system parameters on threat localization accuracy.