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Resource allocation for space domain awareness and synthetic aperture radar




Owens-Fahrner, Naomi, author
Cheney, Margaret, advisor
Mueller, Jennifer, committee member
Shipman, Patrick, committee member
Chandrasekar, Venkatachalam, committee member

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In this thesis, we will address two resource allocation problems. For each of these problems, the objective will be to make use of the resources in an optimal way. We will consider the Space Domain Awareness (SDA) sensor tasking problem as well as the Synthetic Aperture Radar (SAR) flight path planning problem. We will first present a new objective function for the problem of Space Domain Awareness resource allocation (SDARA) as well as a novel algorithm to maximize this new objective function. This SDARA problem aims to maximize the total number of targets seen while minimizing resource costs. These resources, namely the optical sensors, are assumed to be heterogeneous and have different associated tasking costs. The novel algorithm, called the "block greedy" algorithm, provides an approximate regional maximum of this objective function in a tractable amount of time. The block greedy algorithm is a hybrid of the weapon-target-assignment and greedy algorithms. This algorithm will be shown to outperform common algorithms used in solving the SDARA problem. Second, we will present an approach to create an optimal SAR flight path by varying the vehicle's heading, pitch, and antenna steering angles. An optimal flight path is one in which the scene coverage and resolution are maximized. We will utilize the data-collection manifold as a tool to measure scene resolution. We will then add a scene coverage consideration to build an objective function in which we can plan an optimal flight path for an aircraft. After this, we will consider many extensions and applications of using this objective function. These include adding a signal-to-noise ratio (SNR) consideration to SAR flight path planning. Additionally, we will extend this objective function to include multiple unmanned aerial vehicle (UAVs) for optimal flight paths for a SAR system. We will use our objective function to optimally plan flight paths for multiple UAVs.


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