Air and Missile Defense Systems Engineering

The Air and Missile Defense Systems Engineering program, identified as Program Element (PE) 0603327A in the Army's Research, Development, Test & Evaluation (RDT&E) budget, is focused on advancing the Army's capabilities in cyber and electromagnetic activities (CEMA) as they pertain to integrated air and missile defense. The primary goal of this program is to assess, identify, and mitigate vulnerabilities in Army weapon systems including radars, sensors, command and control (C2) systems, and radio frequency (RF) networks against evolving threats in the cyber and electromagnetic spectrum. The program supports both research and prototype development, with a particular emphasis on improving system resiliency and operational effectiveness in contested environments.

Within this program, the FG9: Air and Missile Defense (AMD) Electronic Warfare project is the main line item for FY 2024 and FY 2025. The objectives of this project are to conduct realistic assessments of Army Integrated Fires performance, develop and demonstrate solutions to close identified capability gaps, and implement mitigations against advanced electronic warfare and cyber threats. These efforts are coordinated with other Services and agencies, such as the Missile Defense Agency, to ensure interoperability and leverage joint expertise.

A specific congressional add under FG9 for FY 2024 is Software Memory Protection Methods. The goal of this effort is to reduce the risk of cyber threats targeting missile programs and air and missile defense systems through memory corruption exploits. Activities include evaluating and prototyping software memory protection techniques, applying attack surface reduction methodologies to the software supply chain (including open-source components), and developing technology transition paths for future integration into ongoing missile and air defense programs. This initiative aims to strengthen the cybersecurity posture of critical Army software systems.

Another major congressional add for FY 2024 is Machine Learning for Army Integrated Fires. This project is focused on designing, coding, and integrating machine learning (ML) technologies into the Army's CEMA Detection Algorithm (CDA). The ML enhancements are intended to improve the ability of air and missile defense operators to detect and recognize cyber, positioning, navigation, and timing (PNT), and electronic warfare attacks. The project also assesses the applicability of ML-based CEMA algorithms for use in Army warfighter training aids, simulators, and simulations, thereby supporting both operational and training objectives.

For FY 2025, the Deep CEMA congressional add aims to further advance the integration of machine learning and cutting-edge CEMA technologies. Building on previous ML efforts, Deep CEMA is focused on designing and integrating new ML capabilities and incorporating emerging technologies such as quantum-based hardware, photonics, and physics-based cryptography into existing CEMA detection frameworks. The objective is to enable Army operators to detect and respond to a broader range of advanced threats, including electronic attack, RF-enabled cyber effects, and novel attack vectors. Deep CEMA also supports advanced experimentation and assessment of technology prototypes within Army weapon systems.

The program's activities are structured around a time-phased roadmap, with assessment events conducted approximately every two years in both live and simulated CEMA environments. These events evaluate the effectiveness of proposed solutions and guide future investments. The program maintains continual interface with the intelligence community to ensure awareness of emerging threats and incorporates these insights into future demonstrations and technology development. Funding is also used to develop modeling and simulation tools, including hardware-in-the-loop environments and virtual models for destructive testing, to support comprehensive system evaluation.