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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. Airborne Detect-and-Avoid (ABDAA) and counter-UAS technologies have similar sensing requirements to detect and track airborne threats, albeit for different purposes: to avoid a collision or to neutralize a threat, respectively. These systems typically include a variety of sensors, such as electro-optical or infrared (EO/IR) cameras, RADAR, or LiDAR, and they fuse the data from these sensors to detect and track a given threat and to predict its trajectory. Camera imagery can be an effective method for detection as well as for pose estimation and threat classification, though a single camera cannot resolve range to a threat without additional information, such as knowledge of the threat geometry. To support ABDAA and counter-UAS applications, we consider a merger of two image-based sensing methods that mimic human vision: (1) a "peripheral vision" camera (i.e., with a fisheye lens) to provide a large field-of-view and (2) a "central vision" camera (i.e., with a perspective lens) to provide high resolution imagery of a specific target. Beyond the complementary ability of the two cameras to support detection and classification, the pair form a heterogeneous stereo vision system that can support range resolution. This paper describes the initial development and testing of a peripheral-central vision system to detect, localize, and classify an airborne threat and finally to predict its path using knowledge of the threat class.