Do More with RF Data

Syndication Cloud
Friday, October 11, 2024 at 7:19am UTC

Originally Posted On: https://blog.axellio.com/do-more-with-rf-data

Why Defense Agencies Must Do More with RF Data

In an era where electronic warfare (EW) has become a linchpin of military strategy, American forces are under immense pressure to counter the increasingly sophisticated threats posed by our adversaries.

In this high-stakes environment, the pivotal role that Radio Frequency (RF) monitoring plays cannot be overstated. Beyond merely facilitating troop communications and enabling weapon systems, RF is at the heart of modern military operations, dictating the effectiveness of logistics and strategic planning. This reality is underscored by a recent Pentagon report on China’s military capabilities, which details how the Chinese military considers the electromagnetic spectrum (EMS) to be “an integral component of modern warfare and seeks to achieve information dominance.”  The purpose is “to achieve information dominance in a conflict through the coordinated use of cyberspace and electronic warfare to protect its own information networks and deny the enemy the use of the electromagnetic spectrum.”

RF analysis has quickly garnered almost as much importance as cyber security. However, it requires even more attention for stronger real-time analytic capabilities to be built into programs supported by signals intelligence (SIGINT), electronic warfare (EW), and electronic intelligence (ELINT). RF analysis is essential to enhancing situational awareness, improving target and strike precision, reducing the vulnerability of our troops, and supporting our electronic countermeasures and intelligence gathering. But it has proven to be extremely challenging for military leaders to make effective use of the data that is available.

The RF Challenge

Our adversaries are using ever more sophisticated electronic counter-countermeasures (ECCM) or electronic protective measures (EPM), including signal spoofing and compression. They’re even using AI/ML to manage transmissions or signal behavior, a tactic that can offset countermeasures or detection. To conceal their communication, they are leveraging the power of frequency-hopping spread spectrum (FHSS) to transmit signals across rapidly changing frequencies, across a broad spectrum. This makes wireless communications especially resistant to jamming and can make them difficult to detect.

All this creates an urgent need for SIGINT, EW, and ELINT teams to make better sense of RF information in real-time. However, the amount of collected RF data has grown to levels these systems cannot reliably analyze in real-time while storage and offline analysis are not delivering the detail required.  This escalating challenge manifests in several critical areas, each highlighting the urgent need for innovative solutions to process, analyze, and utilize RF data effectively:

  • Volume: There’s an overwhelming amount of data coming from friendly, neutral, enemy, and civilian transmissions. With an ever-increasing number of wireless communication devices, not just from military devices but also vehicles, operational technologies (OT) and everyday civilian usage, RF is a crowded space, and systems struggle to account for the sheer breadth of data. These days, almost any technical device — from weapons systems to household appliances — is generating RF signals. Within that vast volume of data, it’s difficult to extract the information of importance, which is especially true in urban environments, where a multitude of signals overlap.
  • Capacity: Intelligence, surveillance, and reconnaissance (ISR) sensors need to continuously expand the bandwidth they collect as communication moves into higher frequency ranges. Similarly, civilian mobile telephony is also expanding its frequency range, with 5G devices operating from as low as 450 MHz to over 52 GHz. This requires scanning wider and higher frequency bands, creating more data to provide superior situational awareness. However, this overloads the signal processing applications analyzing the data in real-time, leading to a loss of valuable information.
  • Storage: Due to the shortage of human analysis expertise and real-time processing power at the point of collection, data is frequently recorded and stored for analysis at a later time. With limited local storage capacity due to size, weight, and power (SWaP) issues resulting in performance limitations, the amount of data that can be captured, stored, transported, and analyzed later is often insufficient for comprehensive analysis.

As a result, essential or rogue signals can be missed, and vital information could be lost forever. Solutions that work in civilian life, such as streaming data into the cloud to scale storage and processing, often are not an option on the battlefield. During forward tactical missions, connectivity is often insufficient or unreliable or many times not an option when radio silence is required to avoid detection. And adding more sophisticated processing infrastructure can be expensive and complex, especially at the tactical edge. Yet something needs to change for RF capabilities to meet the current needs. 

The Way Forward

A new approach is required for the military to achieve superior situational awareness and battlefield intelligence. If the intelligence community could analyze, store, and retrieve that data more efficiently and in near real-time, they would enhance the situational awareness so crucial for their success.

With a robust, modernized approach, defense agencies can store, distribute, and analyze RF data more effectively. They’ll get deeper insights into adversarial action, while also extending the useful life of their existing RF analysis infrastructure.

To address these issues, Axellio developed SensorXpress that delivers scalable, simultaneous high-speed RF data recording and distribution, at any instantaneous bandwidth in support of RF collection systems. Learn how Axellio can help at www.axellio.com/sensorxpress

In our next blog, we will discuss what all this means to the analysis infrastructure.