Performance Models for Hypothesis-Level Fusion of Multi-Look SAR ATR

摘要

We present the theoretical basis and a top level system design for estimating and predicting the uncertainty from single and multiple-look model-based automatic target recognition (ATR). Uncertainty estimation is used in decision making based on the probability of correct identification and the probability of a false alarm for a given ATR result. Uncertainty prediction provides a basis for asset management by establishing the value of additional looks at a target. A number of first principles theoretical models have been developed based on information theory and physics. These generally bound performance under idealized conditions. Our hypothesis test approach is designed to support operational uncertainty estimation and prediction based on statistics from parameterized models, simulations, and measurements. A significant challenge that we investigate is generating the probability density of the test statistic under the null hypothesis, which contains un-modeled types and natural clutter. Another challenge we address is establishing uncertainty under multiple look fusion.