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. / Doctor of Philosophy / 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 sensing technologies for detecting and tracking airborne threats have been developed to solve these UAS-related problems. 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 enables estimation of an object location using the different viewpoints of the different cameras (denoted as ``heterogeneous stereo vision.'') A novel peripheral-central vision (PCV) system to detect an airborne threat, estimate the location of the threat, and determine the threat class (e.g. aircraft, bird) is first introduced. To improve the developed PCV system's capability, three novel algorithms for the PCV system are devised: an algorithm to predict the future path of an fixed-wing unmanned aircraft, an algorithm to decide an efficient observation schedule for multiple threats, and an algorithm that provides optimal locations for multiple PCV systems to estimate the threat position better. The performance of algorithms is assessed using an experimental data set and simulations.

Unmanned aerial systems (UAS) have become wildly popular over the past decade. With the increased demand of these systems, it is imperative for agencies such as the Federal Aviation Administration (FAA) to integrate rules and regulations of UAS into the National Airspace System (NAS). In 2023, a UAS Integration Safety and Security Technology Ontology (ISSTO) was developed in the Web Ontology Language (OWL) to aid in this integration. To further aid UAS integration into the NAS, it becomes necessary to develop methods for training new pilots, air traffic control operators, drone operators, etc. This paper proposes a query application that allows new operators to query through ISSTO and efficiently access UAS and counter-UAS information from the knowledge domains contained within the ontology. These knowledge domains include information on various FAA regulations and authorizations, National Airspace classifications, counter-UAS procedures and more. The application developed in this thesis serves as a proof of concept for a commercialized training product.

<p dir="ltr">This thesis adapts and applies methodologies for optimizing the sensing topology of a counter-UAS (CUAS) network to the problem of optimizing the geospatial distribution of emergency response drone bases subject to resource limitations while ensuring alignment with emergency response requirements. The specific context for this work is a 911 call incident response.</p><p dir="ltr">Drone response time, time on scene, and sensor effectiveness are used as network performance metrics to develop a mission planning algorithm that attempts to maximize network response effectiveness. A composite objective function utilizes network response effectiveness and customer-defined region weights that indicate the probability of an incident occurring to represent the performance of the geospatial distribution of 911 drone bases. A Greedy Algorithm iterates upon this objective function to optimize the network topology.</p><p dir="ltr">Previous work [1] suggests that a heuristic based approach utilizing a hexagonal network topology centered around suburban/urban focal points is the preferred method for optimizing the dispatch topology of a 911 response drone network. The optimization strategy deployed here demonstrated an 11% improvement on the objective function compared to this heuristic when tested in Tippecanoe County, IN.</p><p dir="ltr">Previous work [2] also suggests that, of all drones in the design space compliant with FAA Part 107, a single Vertical Take-off and Landing (VTOL) type drone with an ability to transition into fixed wing horizontal flight adhering to specific performance requirements is the preferred drone for executing the emergency response mission. This thesis utilizes the optimization strategy deployed here to test this supposition by comparing the performance of a network with access to only this single drone type to a network with access to multiple types of fixed-wing VTOL drones. Findings indicate that access to only the single type of optimally-sized drone outperforms a network with access to multiple drone types; however, improvements to the greedy algorithm that consider the marginal value of each drone type and across diverse mission types may modify this conclusion.</p>