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The current Space Traffic Management (STM) community was not prepared for the exponential increase in the resident space object (RSO) population that has taken place over the last several years. The combination of poor communication infrastructure and long scheduling lead times of the Space Surveillance Network (SSN) prevent any type of reactive sensor tasking, which is required in event of anomaly detection. This dissertation was designed to survey extensions to the classical notions of covariance based sensor tasking strategies and develop a methodology for evaluating these techniques. A suboptimal partially observable Markov decision process (POMDP) was used as the simulation framework to test various reward functions and decision algorithms while enabling autonomous, reactive sensor tasking. The goal of this work was used the developed evaluation methodology to perform statistical analyses to determine which metrics were most reliable and efficient for Space Traffic Management (STM) of the geosynchronous Earth orbit (GEO) resident space object (RSO) catalog. Hypotheses were tested against simulations of 873 resident space object (RSO) in geosynchronous Earth orbit (GEO) being tracked by 18 heterogeneous, geographically disperse ground-based electro-optical (EO) sensors. This dissertation evaluates the ability of various sensor tasking metrics to produce rewards that maximize geosynchronous Earth orbit (GEO) catalog coverage capability of a sensor network under realistic communication restrictions. / Doctor of Philosophy / Space is getting crowded at an increasing rate. Communication issues and rigid scheduling of the Space Surveillance Network (SSN) prevent reactive sensor tasking, which is needed to alleviate this issue. This dissertation was designed to survey different sensor tasking strategies and develop a methodology for evaluating these techniques. A discrete time estimator called a suboptimal partially observable Markov decision process (POMDP) was used as the simulation framework to test various reward functions and decision algorithms while enabling autonomous, reactive sensor tasking. The goal of this work was used the developed evaluation methodology to perform statistical analyses to determine which metrics were most reliable and efficient for Space Traffic Management (STM) of the geosynchronous Earth orbit (GEO) resident space object (RSO) catalog. Multiple simulation scenarios were evaluated, with the first focused on determining the proper metrics in the ideal sensor network distribution case. From there, hypotheses were tested against simulations of a geographically disperse network of ground-based electro-optical (EO) sensors. This dissertation evaluates the ability of various sensor tasking metrics to produce rewards that maximize geosynchronous Earth orbit (GEO) catalog coverage capability of a sensor network under realistic communication restrictions

This thesis presents experimental and finite element analysis results of eccentrically loaded carbon-fiber composite booms that can deploy solar sails. Using the collapsible tubular mast design along with the geometry from the upcoming Advance Composite Solar Sail System mission, short composite booms segments were manufactured for testing. New clamps were also designed to allow a column bending test to achieve eccentric loading. As buckling through eccentric loading has not previously seen much research, the geometry and composite layups were simplified to allow for ease of manufacturing and verification. The work presented here shows that a finite element simulation, using a new composite material model, can easily simulate the eccentric buckling of collapsible tubular mast booms. It was found that composite booms with this geometry realize two different buckling events. First, local buckles form near each set of clamps, and then a second buckling causes a loss of structural support.

In this work we propose two techniques for symmetric multiple description coding with reduced storage space decoder. The first technique is multiple description scalar quantizer with linear joint de-coders. We propose an optimal design algorithm similar to Vaishampayan's algo-rithm, to which we add an index assignment optimization step. We also solve an additional challenge in the decoder optimization, by proving that the problem is a convex quadratic optimization problem with a closed form solution (under some mild conditions). Our tests show that the new method has very good performance when the probability of description loss is sufficiently low. The other technique is an improvement to the traditional multiple description coding scheme based on uneven erasure protection. We evaluate the asymptotical performance of both schemes for a Gaussian memoryless source. The analysis reveals that the improvement reaches over 1 dB for up to ten descriptions and low probability of description loss. From our experiments we observe that the improved scheme is very competitive comparing to other multiple description techniques as well. / Thesis / Master of Applied Science (MASc)

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