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Full-Text Articles in Engineering
Classification Using Set-Valued Kalman Filtering And Levi's Decision Theory, T.K. Moon, Scott E. Budge
Classification Using Set-Valued Kalman Filtering And Levi's Decision Theory, T.K. Moon, Scott E. Budge
Electrical and Computer Engineering Faculty Publications
We consider the problem of using Levi's expected epistemic decision theory for classification when the hypotheses are of different informational values, conditioned on convex sets obtained from a set-valued Kalman filter. The background of epistemic utility decision theory with convex probabilities is outlined and a brief introduction to set-valued estimation is given. The decision theory is applied to a classifier in a multiple-target tracking scenario. A new probability density, appropriate for classification using the ratio of intensities, is introduced.
Epistemic Decision Theory Applied To Multiple-Target Tracking, Wynn C. Stirling, T. K. Moon, S. E. Budge, J. B. Thompson
Epistemic Decision Theory Applied To Multiple-Target Tracking, Wynn C. Stirling, T. K. Moon, S. E. Budge, J. B. Thompson
Faculty Publications
A decision philosophy that seeks the avoidance of error by trading off belief of truth and value of information is applied to the problem of recognizing tracks from multiple targets (MTT). A successful MTT methodology should be robust in that its performance degrades gracefully as the conditions of the collection become less favorable to optimal operation. By stressing the avoidance, rather than the explicit minimization, of error, the authors obtain a decision rule for trajectory-data association that does not require the resolution of all conflicting hypotheses when the database does not contain sufficient information to do so reliably. This rule, …