Classifier models for SAR ATR performance prediction

• Main Author: Scherreik, Matthew
• Format: Conference Proceeding
• Language: English
• Published: SPIE 07.06.2024
• ISBN: 9781510673823; 1510673822
• ISSN: 0277-786X
• DOI: 10.1117/12.3014458

Automatic target recognition (ATR) performance models (PMs) help engineers and scientists understand the effectiveness of their algorithms as a function of target, environment, and sensor conditions (also called operating conditions or OCs). Traditional approaches typically leverage handcrafted models and rely on subject matter expertise to model the OC-performance relationship using limited amounts of real-world OC data. Recent advances in synthetic data modeling have has led to improved access to high-quality synthetic sensor data. This motivates the consideration of more data-driven PM approaches. In this work, we adopt a probabilistic classification-based framework for ATR performance modeling and explore the use of generic classifiers to predict ATR performance using ATR experiments performed with synthetic data. We leverage results from prior SAR ATR studies to examine the accuracy and calibration performance for regularized logistic regression, multilayer perceptrons, random forests, and Gaussian process classifiers in the performance modeling context. We also use our experiments to make observations regarding the use of these classifiers for performance modeling based on their unique characteristics.