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Uncertainty Estimation For Target Detection System Discrimination And Confidence Performance Metrics, David R. Parker
Uncertainty Estimation For Target Detection System Discrimination And Confidence Performance Metrics, David R. Parker
Theses and Dissertations
This research uses a Bayesian framework to develop probability densities for target detection system performance metrics. The metrics include the receiver operating characteristic (ROC) curve and the confidence error generation (CEG) curve. The ROC curve is a discrimination metric that quantifies how well a detection system separates targets and non-targets, and the CEG curve indicates how well the detection system estimates its own confidence. The degree of uncertainty in these metrics is a concern that previous research has not adequately addressed. This research formulates probability densities of the metrics and characterizes their uncertainty using confidence bands. Additional statistics are obtained …
Multiple Model Methods For Cost Function Based Multiple Hypothesis Trackers, Matthew C. Kozak
Multiple Model Methods For Cost Function Based Multiple Hypothesis Trackers, Matthew C. Kozak
Theses and Dissertations
Multiple hypothesis trackers (MHTs) are widely accepted as the best means of tracking targets in clutter. This research seeks to incorporate multiple model Kalman filters into an Integral Square Error (ISE) cost-function-based MHT to increase the fidelity of target state estimation. Results indicate that the proposed multiple model methods can properly identify the maneuver mode of a target in dense clutter and ensure that an appropriately tuned filter is used. During benign portions of flight, this causes significant reductions in position and velocity RMS errors compared to a single-filter MHT. During portions of flight when the mixture mean deviates significantly …