User fusion to constrain SAR targeting for TSTs

Synthetic aperture radar (SAR) automatic target recognition (ATR) systems will not be effective and efficient without incorporating the user in acquiring and identifying a target. Typically, a SAR-ATR goal is to automatically identify a target for a user; however, in most cases, the data resolution and data availability is not accurate enough to identify the target over all operating conditions. Furthermore, when the target acquisition and recognition cycle is time-constrained, it is important for the SAR-ATR system to quickly present the target list, which the user can edit to reduce the target analysis time. In this paper, we explore user capabilities to assist in a time-sensitive target [TST] recognition task by understanding: (1) user needs, (2) SAR-ATR models and (3) simulation metrics for the SAR-ATR analysis. We utilize the User-Fusion Model, introduced by Blasch and Plano, to analyze the interaction between an image-based SAR-ATR analysis and user actions to facilitate a TST targeting task. Three metrics of throughput, timeliness, confidence, and accuracy are plotted in a novel 3D ROC curve for a given level of throughput to characterize a user-SAR-ATR (USA) model evaluation.

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