21 - 25 April 2024
National Harbor, Maryland, US
Technical Event
Panel Discussion: Machine Learning for Automatic Target Recognition
23 April 2024 • 1:40 PM - 3:10 PM EDT | National Harbor 5 
Automatic Target Recognition (ATR), traditionally rooted in predefined algorithms and rule-based systems, has long been a cornerstone in defense operations. In its conventional form, ATR relied on handcrafted features and rigid frameworks, meeting the challenges of its time. However, the landscape of modern defense scenarios demands a paradigm shift.

In response to evolving complexities, ATR is seamlessly transitioning into the realm of artificial intelligence (AI), embracing a future marked by innovation and adaptability. The traditional rule-based approaches are giving way to dynamic, data-driven methodologies empowered by AI. This shift is not merely a technological upgrade; it represents a strategic move to tackle the intricate challenges of contemporary defense.

The integration of Transformer-based architectures in ATR reflects a fundamental departure from the limitations of predefined algorithms. This evolution is particularly notable in tasks requiring nuanced understanding, such as anomaly detection and trajectory prediction. Moreover, explainable AI (XAI) techniques are becoming integral, ensuring accuracy, transparency, and user trust in ATR systems.

Looking forward, the trajectory of ATR is shaped by the promise of Generative Adversarial Networks (GANs) addressing data scarcity and Quantum Machine Learning optimizing high-dimensional analyses. Academic pursuits like federated learning and meta-learning provide the intellectual backbone for a future-ready ATR landscape.

This narrative unfolds at the intersection of tradition and innovation, where the definition of ATR expands beyond its conventional boundaries, ushering in an era where AI becomes the linchpin in meeting the challenges of modern defense.

Asif Mehmood, Chief Digital and Artificial Intelligence Office (United States)

Zhu Li, Univ. of Missouri (United States)
Shuvra Bhattacharyya, Univ. of Maryland (United States)
Edmund Zelnio, Air Force Research Lab. (United States)
Peter A. Torrione, Covar, LLC (United States)


This panel is part of the Automatic Target Recognition conference.

Event Details

FORMAT: Panel discussion followed by audience Q&A.
MENU: Coffee, decaf, and tea will be available in the exhibition area.
SETUP: Classroom and theater style seating.