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ItemJudicial Evidential Reasoning for Decision Support Applied to Orbit Insertion Failure(Georgia Institute of Technology, 2017-11) Jaunzemis, Andris D. ; Minotra, Dev ; Holzinger, Marcus J. ; Feigh, Karen M. ; Chan, Moses W. ; Shenoy, Prakash P.Realistic decision-making often occurs with insufficient time to gather all possible evidence before a decision must be rendered, requiring an efficient process for prioritizing between potential action sequences. This work aims to develop a decision support system for tasking sensor networks to gather evidence to resolve hypotheses in the face of ambiguous, incomplete, and uncertain evidence. Studies have shown that decision-makers demonstrate several biases in decisions involving probability judgement, so decision-makers must be confident that the evidence-based hypothesis resolution is strong and impartial before declaring an anomaly or reacting to a conjunction analysis. Providing decision-makers with the ability to estimate uncertainty and ambiguity in knowledge has been shown to augment effectiveness. The proposed framework, judicial evidential reasoning (JER), frames decision-maker questions as rigorously testable hypotheses and employs an alternating-agent minimax optimization on belief in the null proposition. This approach values impartiality in addition to time efficiency: an ideal action sequence gathers evidence to quickly resolve hypotheses while guarding against bias. JER applies the Dempster-Shafer theory of belief functions to model knowledge about hypotheses and quantify ambiguity, and adversarial optimization techniques are used to make many-hypothesis resolution computationally tractable. This work includes derivation and application of the JER formulation to a GTO insertion maneuver anomaly scenario.
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ItemMulti-Tiered Approach to Constellation Maneuver Optimization for Low-Thrust Station-Keeping(Georgia Institute of Technology, 2017-02) Jaunzemis, Andris D. ; Roscoe, Christopher W. T. ; Holzinger, Marcus J.This paper presents a multi-tiered approach to constellation-wide optimization of low-thrust station-keeping maneuvers. Starting from the general problem of constellation maneuver optimization, a tractable solution is presented for stationkeeping. The approach utilizes a gradient-descent algorithm to efficiently drive each satellite toward its nominal orbit, encapsulating this trajectory optimization in an outer-loop genetic algorithm to optimize within discrete and non-differentiable constellation-level constraints. The output trajectories are validated and refined in a high-fidelity environment using NASA's General Mission Analysis Tool. A concrete example with operational constraints is presented, and limits of the computation driven assumptions in the tractable solution are assessed.
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ItemEvidence-based sensor tasking for space domain awareness(Georgia Institute of Technology, 2016-09) Jaunzemis, Andris D. ; Holzinger, Marcus J. ; Jah, Moriba K.Space Domain Awareness (SDA) is the actionable knowledge required to predict, avoid, deter, operate through, recover from, and/or attribute cause to the loss and/or degradation of space capabilities and services. A main purpose for SDA is to provide decision-making processes with a quantifiable and timely body of evidence of behavior(s) attributable to specific space threats and/or hazards. To fulfill the promise of SDA, it is necessary for decision makers and analysts to pose specific hypotheses that may be supported or refuted by evidence, some of which may only be collected using sensor networks. While Bayesian inference may support some of these decision making needs, it does not adequately capture ambiguity in supporting evidence; i.e., it struggles to rigorously quantify ‘known unknowns’ for decision makers. Over the past 40 years, evidential reasoning approaches such as Dempster Shafer theory have been developed to address problems with ambiguous bodies of evidence. This paper applies mathematical theories of evidence using Dempster Shafer expert systems to address the following critical issues: 1) How decision makers can pose critical decision criteria as rigorous, testable hypotheses, 2) How to interrogate these hypotheses to reduce ambiguity, and 3) How to task a network of sensors to gather evidence for multiple competing hypotheses. This theory is tested using a simulated sensor tasking scenario balancing search versus track responsibilities.
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ItemEvidential Reasoning Applied to Single-Object Loss-of-Custody Scenarios for Telescope Tasking(Georgia Institute of Technology, 2016-02) Jaunzemis, Andris D. ; Holzinger, Marcus J.Evidential reasoning and modern data fusion models are applied to the single object loss-of-custody scenario in ground-based tracking. Upon a missed observation, the cause of non-detection must be quickly understood to improve follow-up decision-making. Space domain awareness (SDA) sensors, including a brightness sensor and an All-Sky camera with an optical-flow-based cloud detection algorithm, are conditioned as Dempster-Shafer experts and used to assess the cause of a non-detection. Telescope re-tasking is also approached using Dempster- Shafer theory by planning the next observation to minimize an estimated lack of- information. Results from real-world operational sensors show the algorithm's ability to adjust to changing observation conditions and re-task the primary electrooptical sensor accordingly.
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ItemControl Metric Maneuver Detection with Gaussian Mixtures Real Data(Georgia Institute of Technology, 2015-01) Jaunzemis, Andris D. ; Mathew, Midhun ; Holzinger, Marcus J.The minimum-fuel distance metric provides a natural tool with which to associate space object observation data. A trajectory optimization and anomaly hypothesis testing algorithm is developed based on the minimum-fuel distance metric to address observation correlation under the assumption of optimally maneuvering spacecraft. The algorithm is tested using inclination-change scenarios with both synthetic and real data gathered from the Wide Area Augmentation System (WAAS). Comparisons to other commonly-used association metrics such as Mahalanobis distance reveal less sensitivity in anomaly detection but improved consistency with respect to observation cadence, while providing added data through the reconstruction of the optimal maneuver. Non-Gaussian boundary conditions are also approached through an analytical approximation method, yielding significant computational complexity improvements.