PhD Proposal by Andris Jaunzemis

Event Details
  • Date/Time:
    • Tuesday April 4, 2017
      1:30 pm - 3:30 pm
  • Location: MK 317
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Summaries

Summary Sentence: Predictive Sensor Tasking and Decision Support in Space Situational Awareness using Evidential Reasoning

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Name: Andris D. Jaunzemis

Title: Predictive Sensor Tasking and Decision Support in Space Situational Awareness using Evidential Reasoning

 

When: Tuesday April 4, 2017, 1:30pm

Where: MK 317

Committee:

- Dr. Marcus Holzinger (GT-AE)

- Dr. Karen Feigh (GT-AE)

- Dr. Mark Costello (GT-AE)

- Dr. Kim Luu (AFRL AMOS)

- Dr. Travis Blake (Lockheed Martin Space Systems)

 

Abstract

Situation awareness is the perception of elements in the environment, comprehension of their meaning, and projection of their status into the future. Space situational awareness (SSA) is particularly concerned with accurately representing state knowledge of space objects to accurately resolve potential threats, such as collision. Maintaining SSA is essential to the command and control missions of the Joint Space Operations Center (JSpOC). Tracking techniques used in the space surveillance system still rely largely on models and applications from the 1950s and 1960s, while the number of tracked objects continues to grow with improved sensor technologies and ease-of-access to space. This work re-frames the SSA sensor tasking problem to interrogate specific hypotheses using evidential reasoning. First, the spacecraft anomaly detection problem is formulated as a binary hypothesis test using control cost and Mahalanobis distance metrics. This formulation accounts for non-Gaussian boundary conditions to improve applicability to the non-linear dynamic regime of orbital mechanics. Next, a sensor tasking criterion is developed based on reducing ambiguity in hypothesis resolution. This technique tasks sensors to gather the data the leads to the most precise hypothesis resolution possible, and application of evidential reasoning provides a rigorous framework for the inclusion of diverse SSA sensors. The proposed work builds upon this hypothesis-based tasking formulation through a novel approach, judicial evidential reasoning, that enables application to operational SSA scenarios with many hypotheses and many objects. Inspired by game theoretic approaches, judicial evidential reasoning alternates competing priorities to gather support for and against each hypothesis. The application of discrete and combinatorial optimization aids in finding tractable, near-optimal solutions to this very high-dimensional, mixed-integer problem. Finally, the use of specific hypotheses for tasking also motivates a study of effects to the SSA decision-maker of conveying information at the abstraction-level of hypotheses. A cognitive work analysis examines the decision support system elements unique to judicial evidential reasoning that support human decision-making and expertise. By rigorously evaluating hypotheses and conveying this result to the decision-maker at the abstraction-level of hypotheses, this work yields decision-quality information, enables predictive tasking, and improves decision-maker situation awareness and workload.

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Phd proposal
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Mar 31, 2017 - 9:39am
  • Last Updated: Mar 31, 2017 - 9:39am