The challenge

Background and context

Modern military operations are increasingly dependent on EMS for communication, intelligence, surveillance, reconnaissance, and targeting. As cyber and electronic warfare become more intertwined, military forces face significant challenges in maintaining control over the EMS while ensuring operational effectiveness. 

The current approach to EMS management is largely static and relies on manual deconfliction to prevent interference between different users or systems, making it slow, inefficient, and incapable of adapting to the dynamic nature of modern conflicts. To ensure dominance in future conflicts, modern forces require a next-generation, signals-analysis-based EMS management system that provides real-time situational awareness of the electromagnetic environment. Those systems need to be capable of integrating friendly, enemy, and neutral forces’ activities into a single, unified recognized electromagnetic picture (REMP), enabling precision planning for operations making use of the EMS while simultaneously preventing disruption to friendly forces. Current systems lack the ability to dynamically detect and respond to real-time threats and opportunities, leading to increased vulnerabilities and inefficiencies.

Hence, next-generation EMS management systems could leverage advanced signal analytics – including AI, machine learning, deep learning, and rule-based methods – to continuously recognize patterns, detect anomalies, and predict threats so that forces can rapidly respond to emerging challenges, reduce vulnerabilities, and maintain control of the electromagnetic environment.

The ongoing war in Ukraine has demonstrated the consequences of inadequate EMS management in a highly contested battlespace. Both Ukrainian and Russian forces have employed extensive EW and cyber capabilities to disrupt communications, Global Positioning System (GPS) signals, and drone operations. The result has been degraded command and control (C2), loss of situational awareness, and reduced operational tempo. With no real-time REMP, forces on both sides have struggled with unintentional self-jamming and inefficient EMS usage. 

Addressing this capability gap is essential as adversaries continue to evolve their use of the EMS. The future battlefield will be characterized by multi-domain operations where air, land, sea, space, and cyber forces must coordinate seamlessly. Without coordinated, responsive EMS management, friendly forces will struggle to maintain operational coherence in environments where EMS congestion, electronic attack, and cyber threats are constant. By developing an analytic EMS management system capable of operating in these complex electromagnetic environments, defence forces can ensure superiority in an era where control of the EMS is critical to modern warfare.

Essential outcomes

Proposed solutions must demonstrate the following:

• Provide near-real-time (sub-minute latency) visualization and cognitive analysis to understand and predict electromagnetic activities, including signal classification and anomaly detection;
• Provide decision-ready information in support of spectrum deconfliction and decision-making;
• Be developed using an open-architecture approach to ensure interoperability, modularity, and ease of integration with existing and future systems; and,
• Be capable of analyzing data from across the EMS, including radio frequency, infrared, and visual bands, and present the information in a manner that prevents operator cognitive overload and supports effective decision making.

Desired outcomes

Proposed solutions should include capabilities and considerations such as, but not limited to, the following:

• Support for coordination between EW and cyber forces, aligning offensive and defensive operations to maximize effectiveness.
• Resilience to maintain reliable positioning, navigation, and timing (PNT) in Global Navigation Satellite System (GNSS)-denied environments, ensuring continuous operations in the face of adversary jamming.
• Analysis of emerging EW threats, including GNSS jamming (such as signal denial, spoofing, and meaconing), as well as traditional communication jamming and interception, to ensure system resilience and operational continuity. 
• Dynamic adaptation to threats through the optimization of spectrum usage to ensure resilient communications.