EW Success Hinges on Spectrum Dominance, by Nancy Friedrich from Keysight Technologies
/Military forces must continue evolving to stay ahead of their adversaries
By Nancy Friedrich*, AD Industry Solutions Marketing, Keysight Technologies
The US Air Force recently retired its very first “Compass Call” aircraft. Made in 1982, it featured special modifications such as locating, listening, and jamming enemy communications. As a result, this aircraft’s systems could severely inhibit force communications and coordination. These advanced capabilities ensured its operation for almost four decades. In that time, however, the use of the electromagnetic (EM) spectrum operations in conflicts began to evolve at a much faster pace, leading to more complex EW applications. Varied technology and market trends merged to enable the ongoing emergence of new threats, challenging modern military units to identify these quickly evolving threats in a timely manner.
Typically, EW is defined as warfare in the EM spectrum, which means anything operating in the radio frequency (RF) is considered part of EW. Electromagnetic operations target many aspects of the EW environment, ranging from radars and jammers to military communications. Anything that communicates over the air could be a target. EW systems use the EM spectrum to support communications, sensing, and defense. Disarming these capabilities means denying an adversary’s ability to communicate or navigate. Signal intelligence systems also may gather intelligence or find targets.
Even small adversaries can leverage commercially available technology, such as Global Positioning System (GPS) jamming equipment. An attack carried out on navigation, for example, could threaten the military’s ability to synchronize operations. Even more frightening, the possibility exists for the adversary’s cyber system to provide inaccurate position, timing, or navigation information. This possibility could cause problems ranging from confusion to horrific accidents. Threats do not have to conduct a visible attack when they can instead cause failure in communications, coordination, and other operations.
The New EW Landscape
In any EW conflict, the winner is the one that can maneuver most quickly through the EM spectrum by leveraging technology advances. Yet threats have grown increasingly in number and sophistication, with one reason being the availability of technology. A decade ago, very few players dominated this battlefield. The technological capabilities and investments required to dominate in EW prohibited others from developing competing EW capabilities. As commercial electronics became cheaper and more available, however, adversaries of all sizes entered the EW fray. Even smaller adversaries now potentially have a competitive threat arsenal, making the threat environment more dangerous and unpredictable. With the barrier to entry so low, anyone with the right skill and knowledge can secure enough equipment to be a threat.
Software-defined radio (SDR) systems also brought a change to the EW arena. Originally, SDR translated to a reconfigurable radio based only on software. Analog-to-digital conversion occurred directly at the antenna. Modern SDRs often take more complex forms, however, changing their operating frequency, modulation, operating bandwidth, and network protocol without having to change the system hardware. As speeds increase for both digital signal processing (DSP) and analog-to-digital converters (ADC), more signal processing occurs digitally. By leveraging such systems, military forces can more easily upgrade their threat systems. The rapid pace of change for commercially available dual-use technologies and software-defined systems is driving much of the diversity and complexity of future threats.
In the future, the biggest impact to EW is predicted to come from artificial intelligence (AI) technologies. The pace of change for commercially available dual-use technologies and software-defined systems greatly drives both the diversity and complexity of future threats. With the addition of AI, those threats will also learn from each conflict - making them more likely to prevail in the future.
Threats Take Varied Forms
As a result of these technology leaps, threats grow increasingly sophisticated. Threats of the past were static in nature – always appearing and behaving the same. Today’s threats are responsive, changing their behavior based on the scenario. If an adversary is jamming a reactive threat, for example, it will switch frequencies or take another action to elude that jamming. Adversaries must now assume that a threat might change and prepare to react accordingly.
Often, such threats are described as cognitive or adaptive. Although people use these terms interchangeably, many levels of adaptability exist. Most of them do not come near the capabilities of cognitive EW. Using machine learning, cognitive EW systems can enter an environment with no knowledge of the adversary’s capabilities and rapidly understand the scenario. By doing something that makes the adversary’s system react, they can evaluate its response. They can then develop an effective response that is suited for that particular adversary’s system.
In contrast, adaptive solutions cannot rapidly grasp and respond to a new scenario in an original manner. For example, an adaptive radar can sense the environment and alter transmission characteristics accordingly, providing a new waveform for each transmission or adjusting pulse processing. This flexibility may allow it to enhance its target resolution, for instance.
Many adversary systems require only a simple software change to alter waveforms, which adds to the unpredictability of waveform appearance and behavior. Military forces struggle to isolate adaptive radar pulses from other signals, friend or foe. As these threats grow increasingly adaptive, their opponents must respond to them in a much shorter time.
Impact of Machine Learning
With AI, intelligent machines work and respond much like humans. Machines can therefore perform smarter tasks using capabilities like signals recognition. Machine learning takes AI one step further, allowing machines to continuously learn from data and adapt as a result. These computers learn over time at a very rapid rate. Threats using machine learning continue to learn from every conflict, determining ways to be more effective so that they prevail against future countermeasures.
This evolution occurs without the need for human interaction, as the computer decides how to alter behaviors. When tested or engaged, these threat systems learn from that experience. They modify their future behavior as a result, which means the computer decides the next steps. Due to the system’s unpredictable behavior, even the people who implemented it cannot foretell its exact behavior.
As threat systems advance with machine learning technology, they will adapt and alter their behavior or course of action at an increasingly rapid rate. If a radar is trying to track a jet, for example, the adversary’s countermeasures may stop it from succeeding. Using machine learning, that radar would repeatedly try new approaches in an effort to achieve success. Today’s machines possess intelligence that is an order of magnitude higher than a human expert in EW, as they learn from data that continues to aggregate.
An Ever-Changing Future
Due to the abundance of new, modern, responsive threats, military forces vie for control of the EM spectrum. Spectrum dominance enables them to detect, deceive, and disrupt enemy forces while protecting their own military. Should this dominance be achieved, military forces must constantly innovate their EW threats and countermeasures to stay in that leading position. To keep pace with the ever-changing threat environment, military forces demand flexible, scalable solutions. A risk mitigated today may not be an issue six months from now, putting military forces in the position of always facing a new threat or even foe.
Incomplete, disaggregated data prevents military forces from attaining or creating a clear threat picture. They lack a methodology against which they can test these threats. This issue stems from traditional EW threat simulation systems. They use databases of known threats, which usually have associated countermeasures. Such classified lists of known targets are no longer as effective, as they quickly fall out of date. These systems were not built to identify and isolate threats in the EM environment and determine countermeasures on the fly.
Even when capable of processing new signals, such systems involve a very time-consuming process. From the theater itself, military forces collect information about a type of signal, such as frequency or pulse repetition interval (PRI). They send that information to a lab, where it is analyzed to gather more information and develop countermeasures. Months pass before that information is available in the system for use.
In the theater of the future, the adversary will have a more complete picture of operations. Building on the past decade’s transformation, the next 10 to 20 years promise to deliver faster, more evolved technology developments. Many predict that machine learning and artificial intelligence developments will drive powerful, continuous evolution in EW. The EW threat environment will leverage drastic processing improvements, for example, using multiple devices to provide more information in less time. Sensing technologies also will play a larger role, gathering information about the conflict zone. New coding techniques already result in increasingly complex, interconnected, and correlated sensors.
These technology innovations will spawn knowledgeable, newly responsive threats that find novel ways to gain power in the EM spectrum. While the technologies will continue evolving and new threats constantly emerge, one constant remains: the military force that achieves and maintains spectrum dominance also will dominate the EW theater.
Nancy Friedrich is an Industry Solutions Marketer for Aerospace & Defense at Keysight Technologies. She joined Keysight after two decades working on engineering media brands, eventually serving as Executive Director of Content for a family of brands including Electronic Design, Microwaves & RF, and Machine Design. Nancy later served as Editor-in-Chief of Design News and Content Director for tradeshows including DesignCon, ESC, and the Smart Manufacturing shows.
Photos credits: Keysight Technologies, Earthcube
