Operating condition sampling strategies for evaluating ATR performance

Shaun Stephens; Jacob Ross; Charles Hill; William Coleman; Matthew Scherreik

doi:10.1117/12.3013307

7 June 2024 Operating condition sampling strategies for evaluating ATR performance

Shaun Stephens, Jacob Ross, Charles Hill, William Coleman, Matthew Scherreik

Proceedings Volume 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI; 130320E (2024) https://doi.org/10.1117/12.3013307
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Synthetic data is commonly used to assess the performance of Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems modeling the OC space in question. In this work we demonstrate that the use of an informed sampling technique compared to an uninformed sampling approach can efficiently assess the “OC gap” between train and test OC spaces as the gap narrows. To demonstrate the effectiveness of an informed sampling approach, SAR ATR experiments are conducted as a function of how representative the train distribution of OCs are compared to the test OC space given a variety of challenging OC scenarios. Algorithm performance is assessed over a series of experiments given discrepancies between azimuth and depression angle of the sensor.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaun Stephens, Jacob Ross, Charles Hill, William Coleman, and Matthew Scherreik "Operating condition sampling strategies for evaluating ATR performance", Proc. SPIE 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI, 130320E (7 June 2024); https://doi.org/10.1117/12.3013307

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