Automatic target recognition (ATR) has evolved beyond rule-based and manually engineered approaches and is now driven by advanced machine learning models that generalize across sensors, missions, and domains. Three developments have been particularly impactful. First, transformer-based architectures and large-scale self-supervised learning enable robust visual and scene representations that transfer across electro-optical, infrared, radar, SAR, and space-based modalities, while reducing reliance on labeled data. Second, advances in multimodal fusion improve integration of ground, air, maritime, and orbital data, supporting persistent tracking of small, low-observable, and maneuvering targets in complex environments. Third, generative AI and high-fidelity simulation are increasingly used to produce realistic training datasets for rare and emerging targets, addressing data scarcity.

These advancements are enhancing ATR effectiveness in mission areas such as integrated air and missile defense, maritime domain awareness, and space domain awareness, where sensing conditions vary widely. Modern ATR systems also incorporate confidence scoring, uncertainty quantification, and interpretable outputs to support human decision-making, reduce operator burden, and promote responsible use.

Despite this progress, key challenges remain. Ensuring robustness under adverse conditions such as compression, jamming, spoofing, and data degradation is critical. Comprehensive evaluation across diverse modalities and operational settings is still limited, and MLOps practices for continuous updates in secure environments remain immature. Broader issues related to autonomy, ethics, and policy also require attention.

This panel will bring together researchers and practitioners to examine advances and identify gaps, including transformer-based methods, multisensor fusion, synthetic data generation, uncertainty-aware decision support, and end-to-end ATR systems spanning data engineering, model development, deployment, and lifecycle management. The goal is to define a path toward ATR systems that are accurate, resilient, explainable, and aligned with future operational needs.

Moderators:
Asif Mehmood,Global InfoTek, Inc.

Panelists:
Vijayan K. Asari, University of Dayton / Vision Laboratory
Olga Mendoza-Schrock, Air Force Research Laboratory
Hunter Moore, Hardshell
Kristen P. Jaskie, Prime Solutions Group

This panel is part of the Automatic Target Recognition conference.

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Event Details

FORMAT: Panel discussion followed by audience Q&A.
MENU: Coffee, decaf, and tea will be available at the coffee service stations during published times.
SETUP: Classroom and theater style seating.

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