Score-based SAR ATR performance model with operating condition dependencies

Abstract

Automatic target recognition (ATR) performance models are needed for online adaptation and for effective use (e.g., in fusion) of ATR products. We present empirical models focused on synthetic aperture radar (SAR) ATR algorithms. These models are not ATR algorithms in themselves; rather they are models of ATRs developed with the intention of capturing the behavior, at least on a statistical basis, of a reference ATR algorithm. The model covariates (or inputs) might include the ATR operating conditions (sensor, target, and environment), ATR training parameters, etc. The model might produce performance metrics (Pid, Pd, Pfa, etc.) or individual ATR decisions. "Scores" are an intermediate product of many ATRs, which then go through a relatively simple decision rule. Our model has a parallel structure, first modeling the score production and then mapping scores to model outputs. From a regression perspective, it is impossible to predict individual ATR outcomes for all possible values of this covariate space since samples are only available for small subsets of the total space. Given this limitation, and absent a purely theoretical model meaningfully matched to the true complexity of this problem, our approach is to examine the empirical behavior of scores across various operating conditions, and identify trends and characteristics of the scores that are apparently predictable. Many of the scores available for training are in so-called standard operating conditions (SOC), and a far smaller number are in so-called extended operating conditions (EOCs). The influence of the EOCs on scores and ATR decisions are examined in detail.