Association of Old Crows (AOC)

This webinar will explore the effects of the modern electronic battlefield on the performance or non-performance of manned and unmanned air vehicles, and the methods used to mitigate the effects of electromagnetics on their operation.

• CEMA 3-4 Conference presentation, “Multifunctional Composite for Electromagnetic Shielding”
• AHS Specialists Meeting, “Multi-Functional Structural Composite with Integrated Electromagnetic Shielding”.
• Harry R. Luzetsky.  “Multifunctional Structural Composite with Integrated Electromagnetic Shielding”. Aircraft Survivability Journal 19 Spring Issue 2019. Pages 9-17.
• Harry R. Luzetsky.  “Advanced Composite Solutions for Dynamic Structural Applications.”  DSIAC Journal Volume 3 Number 3 Summer 2016. Pages 24-30.
• Harry R. Luzetsky, Martha Klein, and Graham Ostrander.  “Multifunctional Structural Composite with Integrated Electromagnetic Shielding.”  May 08, 2017 (Presented at AHS Forum 73)
• Robert A. Haynes, Harry R. Luzetsky and Ellen M. Phifer, “Shielding Multifunctional Composites for Aircraft Structures”, AIAA SCITECH Forum 6-10 January 2025.
• Harry R. Luzetsky, “UASs in the Modern Electronic Battlefield”, Volume 9 Number 1, 
• Harry R. Luzetsky,” Advanced Composite Solutions for Dynamic Structural Applications” DSIAC Journal Volume 3 Number 3 Sumer 2016, pp23-30.

AI is revolutionizing every aspect of our lives from entertainment, medicine, and sports to global supply chains and national security, triggering a new era characterized by rapid technological breakthroughs. This multifaceted era extends beyond AI algorithms alone, encompassing advances in semiconductors and emerging technologies such as digital twins, highlighting that AI’s influence relies on the convergence of a complex ecosystem of interdependent technologies rather than isolated innovations. Drawing on recent success stories and real-world applications, this presentation highlights how AI is already improving decision-making, creating breakthroughs, enhancing operational efficiency, and enabling new forms of innovation across sectors. As we look ahead, we discuss how 2026 is emerging as a pivotal year in which AI evolves from a powerful instrument into a collaborative partner, transforming how we work, innovate, and solve problems. The presentation concludes with a realistic glimpse into the future of AI-driven innovation and its impact.

Missiles provide the essential accuracy and standoff range capabilities that are required in modern warfare. Technologies for missiles are rapidly emerging, resulting in the frequent introduction of new missile systems. The capability to meet the requirements for missile survivability is especially important for long range missiles. This presentation provides an overview of the broad range of alternatives in satisfying missile requirements for survivability. The methods presented are generally physics-based, to provide insight into the primary driving parameters. Typical values of missile parameters and the characteristics of current operational missiles are discussed, as well as the enabling subsystems and technologies for missiles and the current/projected state-of-the-art. Videos are presented to illustrate missile development tests and facilities and missile performance.

APPROACHES FOR MISSILE SURVIVABILITY ADDRESSED IN THIS PRESENTATION

• Speed
• Altitude
• Maneuvers
• Low observable airframe
• CCM seeker
• Inertial navigation system
• Pseudolites
• Data link
• EMP hardening
• Threat avoidance/mission planning

The application of artificial intelligence (AI) represents the next major advancement in electronic warfare (EW). With the field of AI, as well as EW, constantly evolving, it is critical to form a baseline understanding of the impact of cognitive EW (CEW). This session seeks to first provide a framework for CEW test and evaluation (T&E) planning and production to prepare the T&E and supporting modeling & simulation, academic, and intelligence communities for CEW. Next, an evaluation of Human-Autonomous teams (HATs), or humans and AI with agency working together will be evaluated. This session will cover a diverse set of topics to include the Superteams impact on CEW, including explorations of current AI and machine learning (ML) research within EW, investigation into possible test methodologies for neural networks, HATs, Superteams, and AI-enabled systems, and considerations for AI risk and ethics assessments within T&E. Conclusions were reached through academic, industry, and government analysis, expert interviews with existing EW laboratories and test facilities, and collaboration with AI, HATs, and Superteams experts to determine the capacity for CEW, demonstrations of CEW concepts in hardware-in-the-loop (HWIL) modeling and machine learning operations (MLOps) pipelines, and assessment of electronic attack (EA) research through physical execution of principles. The rapidly changing field of AI coupled with the nuance of EW functions presents sophisticated problems only able to be tackled through community collaborative efforts.

AOC Members – $190

Government/Military – $190

Non-Members – $405

Questions related to registration can be directed to Samantha Kim, Professional Development Coordinator, at [email protected].

AOC Members – $750

Government/Military – $750

Non-Members – $1,000

Questions related to registration can be directed to Samantha Kim, Professional Development Coordinator, at [email protected].

Course Location: West Virginia University (WVU) Campus

Course Length: 4 sessions

Description: Held at West Virginia University (WVU), this workshop leverages cutting-edge research, processes, and capabilities while teaching the next generation of engineers and scientists the foundations of Integrated Air and Missile Defense (IAMD) T&E and threat engineering. Partnering with small businesses, the approach not only transitions technology into the broader test and evaluation workforce but also cultivates advanced degree graduates (master’s and PhD) who possess expert-level knowledge of threat asset design strategies, relevant technologies, and threat engineering methods. Once in the workforce, these graduates can guide T&E engineering decision-makers and resource managers in acquiring near- and far-term threat-representative resources – both physical and M&S – in a fiscally responsible manner.

Who Should Attend: The audience should have a technical background in engineering or applied physics associated with one or more of the following areas: weapons, missiles, electromagnetic systems and techniques, machine learning techniques, and engagement level modeling and simulation of complex weapon systems.

Networking: This four-day workshop will be held on the engineering campus of West Virginia University. Specialized VIP tours and general participant tours of campus facilities will be available. Various scheduled breaks, an evening reception, and events throughout the workshop are designed to facilitate collaboration and foster professional connections with attendees, instructors, and leading experts from the DOD and industry.

AOC Members – $750

Government/Military – $750

Non-Members – $1,000

Questions related to registration can be directed to Samantha Kim, Professional Development Coordinator, at [email protected].

Modern Electromagnetic Warfare Operations (EMO) relies on mission datasets that predict and catalogue emission signatures to support emitter Identity (specific source) and Recognition (type or category). These datasets feed the Recognised Electromagnetic Picture (REMP) and the Common Operating Picture (COP). However, real-world conditions often diverge from predictions: variations in equipment, probabilistic signal observations, and incomplete TECHINT analysis can introduce uncertainty.

When Identity or Recognition is ambiguous, geolocation accuracy also suffers. For example, multiple identical emitters observed through Lines of Bearing (LOB) can generate numerous candidate positions, of which only some are correct. As emitter density increases, this ambiguity grows exponentially, degrading Situational Awareness (SA) and operational decision-making.

This session explores the underlying problem space of LOB-based geolocation under ambiguity, including how observer count, altitude, and system scaling influence the number of candidate solutions and the probability of identifying the correct emitter location. It introduces a geometric, equipment-agnostic discrimination method based on a graph data-structure with probabilistic agreement between observer pairs, enabling probability imbalance to isolate true solutions.

Unlike approaches that rely on tracking, parametric comparison, or Machine Learning (ML), this method operates at the detection stage, reducing false solutions without added latency or trust concerns. It supports coalition operations by decoupling reliance on emitter identity and enabling geolocation of previously unencountered emitters. Operational use cases demonstrate how asset tasking can further bias probabilities to improve solution accuracy, preserving SA in complex and dynamic EW environments.

LEARNING OBJECTIVES: Attendees will learn/takeaway:

• Outcome 1: Understand the challenges of Line of Bearing (LOB) geolocation and how emitter ambiguity degrades accuracy and situational awareness.
• Outcome 2: Analyze how system factors—such as number of observers, altitude, and linear scaling—lead to exponential growth in false geolocation solutions.
• Outcome 3: Apply a graph-based, equipment-agnostic geolocation discrimination method that reduces false solutions and supports coalition EW operations without reliance on tracking or ML

TARGET AUDIENCE:
EW practitioners, particularly those involved in SIGINT analysis and geolocation.

The Chinese People's Liberation Army has been undertaking a massive modernization effort that will see it "fully mechanize, fully informationized, and fully intelligence-ized." The "intelligence-ization" phase of Chinese modernization is about exploiting the most recent advances in information technology, including aspects of artificial intelligence, but also big data. 

This presentation examines how artificial intelligence is enabling a fundamental shift in unmanned platform RF architecture — replacing discrete radar, communications, jamming, and ESM payloads with a single integrated low-SWAP multifunctional RF system. The key enabler is AI's ability to switch and orchestrate shared apertures and waveforms across all functions in microseconds, continuously rebalancing mission priorities as the electromagnetic environment evolves. We explore the AI technologies driving this capability — from reinforcement learning-based aperture scheduling to cognitive waveform synthesis — while candidly addressing the core tradeoffs: performance versus resource sharing, switching speed versus reasoning depth, and adaptability versus training data dependency. For drone platforms where every gram and cubic centimeter is contested, AI-enabled multifunctional RF is not an incremental improvement — it is the only architecture that makes full-spectrum capability physically possible.