Can the U.S. maintain its dominance in electronic warfare?

Story

August 05, 2024

The defense community needs to harness the power of big data in the context of electronic warfare (EW) to maintain its advantage in this crucial domain.

Spectrum dominance – the essence of EW

Any military operation, whether by land, sea, air, space, or even in cyberspace is now heavily dependent on access and visibility into the electromagnetic spectrum. To support both tactical and strategic EW activities, visibility across the electromagnetic spectrum is crucial to detect, identify, and locate friendly, enemy, or neutral sources of:

• intended radiation of electromagnetic energy such as communication equipment (mobile phones, radar, or microwave communication), or
• unintended radiation of electromagnetic energy such as computers or weapon systems.

Monitoring the spectrum means collecting, storing, and analyzing the vast amounts of RF [radio-frequency] data which creates the quintessential big data challenge that exceeds the capabilities of traditional data processing systems, especially in the size, weight, and power (SWaP)-constrained military tactical edge environments. (Figure 1.)

[Figure 1 ǀ A diagram shows the multidimensional challenge involved in gaining flexibility into the electromagnetic spectrum for electronic warfare. Graphic courtesy Axellio.]

The battle for dominance in the electromagnetic spectrum has been ongoing for almost a century now. Its importance is more crucial than ever now as it provides a decisive advantage in conflicts due to the reliance on electronic equipment on today’s battlefield. Military doctrines around the world emphasize the use of big data analytics to optimize EW countermeasures, enhance the precision of jamming techniques, and enable cognitive EW attacks that can autonomously adapt to the ever-changing conditions on the battlefield.

In addition, new advancements in artificial intelligence (AI) to detect and identify RF signatures in near-real-time enhance the threat landscape, but also significantly increase the amount of data that need to be captured and analyzed.

The U.S. approach to EW: Catching up

While the U.S. has historically been a leader in EW, focusing resources largely on counterterrorism over the past two decades has slowed modernization of some EW systems. Many of these systems employed by the U.S. armed forces are outdated, hampered by data silos and limited processing power. They lack the ability to effectively analyze the increasingly complex signals and vast data volumes generated by modern communication systems in real-time operations.

Whereas countries like China have modernized more than 80% of their EW units over the last 15 years, a “Center for Strategic and Budgetary Assessments” report states that the U.S. may need at least until the end of this decade to close this gap. The Pentagon has initiated promising efforts to accelerate EW innovation, such as the Air Force’s Electromagnetic Spectrum Superiority Strategy and the Navy’s Electromagnetic Maneuver Warfare. However, the pace of progress in harnessing the RF analytics and the resulting big data problem remains a concern, as potential adversaries continue to make rapid advancements in this field.

Navigating the data deluge

The explosion of RF data from proliferating signals is straining the real-time processing limitations of the existing systems employed by U.S. forces. This reality has resulted in vital information being missed or discarded, as storage and offline analysis procedures struggle to keep up with the rapidly changing EW environment.

The immense amount of RF signals originating from diverse sources make it challenging to identify and extract essential information. As the frequency spectrum used for communication continues to broaden, RF sensors must evolve to capture a wider range of signals. This increased data acquisition taxes signal-processing systems, potentially causing the loss of crucial intelligence.

Due to constraints in real-time processing capacity and human analysis resources at the point of collection, RF data is often stored for subsequent analysis. However, the SWaP limitations in processing and storage capacity can result in incomplete data capture and inadequate information for comprehensive analysis. (Figure 2.)

[Figure 2 ǀ The RF challenge: A diagram shows how the numbers of sensors and the exponential growth of RF data can overwhelm analysis systems. Graphic courtesy Axellio.]

Although cloud-based storage and processing solutions have proven effective in civilian applications, they are frequently impractical in combat situations due to unreliable connectivity. Upgrading to more sophisticated processing infrastructure can be expensive and challenging, particularly at the tactical edge. Nonetheless, it is crucial that RF capabilities advance to tackle these obstacles and preserve the competitive edge in the EW domain.

Harnessing the big data problem in RF analysis: The key to EW dominance

The progress made in AI and machine learning (ML) opens an opportunity to regain leadership in this highly contested spectrum; however, these applications require vast amounts of data and processing, which creates additional challenges for the analysis infrastructure.

Big data in the context of RF analysis refers to the vast amounts of information collected from the electromagnetic spectrum. Extended time-on-target at the widest instantaneous bandwidth possible is crucial for RF analysis to gain a competitive advantage and to provide immediate, actionable insights.

To gain a tactical advantage in the modern battlefield, military operators must be able to analyze a wide range of frequencies and complex signal environments in real time. However, the current RF analysis systems are often inadequate for this task. Overwhelmed by the sheer volume of data, operators may make compromises, limiting the spectrum they monitor and analyze and ultimately leading to incomplete exploitation of RF data and potential mission failure.

The key to overcoming this challenge is the implementation of an intelligent signal data distribution system. By capturing, buffering, and simultaneously distributing incoming RF data streams to multiple analysis applications, analysts can ensure that the existing infrastructure is utilized to its fullest potential. This approach optimizes the performance of the analysis systems by regulating the flow of RF data, preventing data loss and premature filtering. Moreover, it extends the lifespan of the current RF infrastructure by mitigating the risk of overload while maximizing its capabilities.

Maximizing post-mission analysis through extended spectrum recording

In the EW era, the ability to capture and record the electronic signatures of adversaries and joint forces is paramount for the safety of the missions as well as for the development of effective countermeasures. As the signal environment becomes increasingly congested, it is essential to conduct extended monitoring to obtain a comprehensive understanding of the electromagnetic landscape.

However, this prolonged monitoring leads to a significant increase in the volume of RF data that needs to be processed and stored. The sheer quantity of information can easily surpass the storage capacity of the analysis systems, creating a bottleneck in the intelligence-gathering process. Overcoming the SWaP constraints for signal capture and data storage is also critical, especially to maneuver forces at or near the forward line of troops.

Leveraging modern storage technology enables military leaders to store captured RF data for extended periods while minimizing SWaP impacts. This step enables a paradigm shift in post-mission analysis by decoupling the RF data from the sensor and facilitating repeated, in-depth examination. By storing the data for longer durations, analysts can replay the information through various applications, conducting meticulous assessments and developing more effective countermeasures. This approach empowers the military to analyze a higher density of frequency spectrum over an extended time frame, leading to more accurate intelligence and better-informed decision-making.

Optimizing RF solutions for autonomous tactical operations

Especially for forward tactical missions, tactical deployment, and concealment, strict SWaP constraints play a crucial role in determining the mission’s outcome. One of the most critical aspects is the ability to operate independently, without relying on data backhaul, which can be slow or unreliable in hostile environments.

This requires development of highly compact, purpose-built solutions specifically designed for rapid deployment, easy transportation, and quick setup in tactical situations. These compact systems enable comprehensive RF data collection even in challenging environments, providing the real-time, actionable intelligence necessary for quick decision-making, even in the most disconnected and non-tethered operations.

Moreover, the effective utilization of big data in RF analysis requires not only advanced technologies but also a highly skilled workforce. The U.S. must invest in training and education programs to cultivate a new generation of EW professionals who are adept at leveraging data science and AI/ML to extract valuable insights from complex RF datasets. Collaboration with industry leaders at the forefront of RF data processing can help bridge the talent gap and accelerate the adoption of data-centric EW capabilities across the U.S. military.

Going forward

As the electromagnetic spectrum becomes increasingly contested, the ability to harness big data in RF analysis will be a defining factor in achieving EW dominance. The rapid advancements in EW capabilities by adversaries present a formidable challenge to U.S. national security, threatening to erode the technological edge that has long been a cornerstone of American military power.

The path forward requires a strategic emphasis on the development and deployment of advanced data-processing solutions tailored to the unique demands of modern RF analysis. By investing in cutting-edge technologies, fostering a skilled EW workforce, and collaborating with innovative industry partners, the U.S. can harness the power of big data to enhance its EW capabilities and maintain its military superiority. RF monitoring and analysis plays a crucial role in providing the necessary visibility across the electromagnetic spectrum to achieve EW dominance.

Scott Aken is chief executive officer of Axellio, which accelerates the performance and insight of analysis for cybersecurity and RF solutions. Previously, Scott was president of Charon Technologies, a subsidiary of CACI International. Scott has also held key leadership roles at L-3 Communications and SAIC, developing corporate-wide cyber strategies and product/solution offerings while determining key cyber investments. Scott built his cyber expertise as a special agent with the FBI, where he conducted numerous cyber-counterintelligence computer-intrusion investigations and was a member of its elite Cyber Action Team. Prior to his career at the FBI, Aken spent a decade working in the software and internet industry, holding leadership positions at VeriSign/Network Solutions and GE.

Axellio • https://www.axellio.com/