Software-defined radio (SDR), or virtual radio, is creating an unprecedented, “knowing sea” and will profoundly challenge the Navy’s ability to exploit and defend the electromagnetic spectrum on which it depends.
To imagine the devastating effects of disruptive technologies unharnessed, picture a littoral engagement with a capable adversary who has degraded our space-based reconnaissance–NTM (national technical means)—by jamming or direct attacks. We of course have organic and tactical assets, but our active sensors betray our location. The enemy deploys swarms of cheap, smart sensors on drones and autonomous surface craft that quickly size up our capabilities and intentions. We had been working on similar technologies, but were frustrated by cost overruns, bureaucracy, expanding scope, and acquisition headaches. The sea is alive with environmentally aware SDR sensors and jammers that make basic tactical decisions. Unfortunately, most of these smart sensors belong to the adversary. Having lost dominance of the electromagnetic spectrum, we are blind and vulnerable.
SDR is creating a new, level field globally as key technologies mature and nascent technologies move forward. Research in machine learning, deep learning, reinforcement learning, and artificial intelligence (AI) augmented by big-data technologies is exploding, enabled by powerful computers, programmable chips such as field-programmable gate arrays, cloud computing, and advances in machine learning. While we were sleeping, the pieces of the puzzle were coming together in surprising ways. The Navy is now confronted with SDR capability far beyond its original vision. The questions we must answer today are:
- What is SDR and what are the enablers for cheap SDR?
- How can we exploit smart and ubiquitous collectors?
- What might we and our adversaries do with SDR?
- What are the predictable effects of big data, machine learning, and artificial confluence?
- Does this spell SIGINT for the masses? The death of NTM?
SDR can be explained in layman’s terms:
In traditional hardware radios, the mathematical operations required to decode and process radio signals are performed using analog circuitry. . . . Computers have become powerful enough to perform the required mathematical calculations in software. . . . This has led to the creation of advanced radios that previously required complicated analog hardware but are now able to be implemented easily in software. Advanced radio capabilities such as wideband tuning and waterfall displays are now available at much lower costs, due to SDR.[1]
Moore’s Law and SDR
Put simply, you can create a different radio just by changing the software. Consistent with Moore’s law predicting higher semiconductor complexity and lower price, microprocessor advances for highly capable radios and RF applications are expected to allow individuals to communicate with whomever they wish, whenever they want, and in whatever manner they choose without switching hardware. Big-data technologies play a significant role by offering parallel processing and other techniques to maximize the power of the processor.
We are now a few years beyond the glitches that compromised SDR’s debut, involving JTRS (Joint Tactical Radio System) scope and cost overrun problems. Today, we can expect an SDR, consisting physically of a USB dongle, for as low as $20. With this dongle and open-source software, anyone can cheaply monitor aircraft flights on a map; transmit automatic dependent surveillance–broadcast technology; capture images from weather satellites; analyze radar readings with spectrum-analysis software; listen to FM radio; and use it as a GPS, baby monitor, or cheap radar detector. SDR, supercharged by Moore’s Law, can serve as a universal radio, which (with the right software) would have cost thousands or hundreds of thousands of dollars in the past. Cheap SDR could be embedded in small mobile systems that serve as a poor man’s electronic intelligence/electronic warfare, and open-source machine learning augmented with big-data analytics could be used to automate data-mining process.
For our adversaries, such technology will be as world flattening as anything Thomas Friedman imagined. With open access to websites like Github.com, anyone can download software for a virtual radio or analytic tool, or visit sigidwiki.com to identify radio signals, heat maps, and waterfall images. The popular mathematical software MATLAB already allows the development of powerful SDR applications. As soon as crowd sourcing, hobbyists, and small-scale innovators work out the kinks, this technology will inevitably be adopted as a prime tool for bad actors.
Beyond JTRS—Why the Navy Needs SDR
In the 2018 summary, DoD’s National Defense Strategy (NDS) states that “interstate strategic competition, not terrorism, is now the primary concern in U.S. national security.”[2] China (or Russia/Iran) may, in a conflict, employ A2/AD (anti-access area denial), pushing our maritime forces away from China’s first-island chain, which “forms a key geographical baseline for China’s maritime sphere of influence.”[3] A Chinese white paper discussed non-kinetic methods of denying adversary access to the islands’ space and cyber domains and electromagnetic spectrum. China seeks to use electromagnetic-pulse weapons to reduce freedom of action in multiple domains by destroying early-warning, detection, command, and information systems. Without listing specific capabilities, Chinese planners discussed using GPS and radar jamming to deny information and weaken the adversary for kinetic attack.[4] This denial would extend to space.
Naval responses to counter or mitigate the adversary’s use of A2/AD may include the distributed lethality concept that seeks to increase the surface force’s offensive by employing them in dispersed offensive formations known as “hunter-killer SAGs.”[5] Cheap, smart, ubiquitous SDR sensors could mitigate A2/AD threats and provide sensor data when our adversaries or the intel gatekeepers deny NTM. Cognitive (intelligent) radio powered by SDR could learn about its RF environment and interpret SIGINT as would a human analyst. We cannot have smart cognitive/intelligent radios using machine learning without SDR. Machine learning-processing RF at point of collection would have the benefit of conserving all information and provide quicker analysis. DARPA and others are researching cognitive EW radio (intelligent/cognitive radio). While RF features and modulation are complex data structures—more complex than machine vision—they offer a promise of cognitive radio.
A few examples of SDR capabilities relevant to the Navy include:
- Mobile sensors with environmental awareness may be enabled with LIDAR (a relative of SDR).
- Distributed lethality and other maritime operations may require EMCON (radio silence). At such time, SDR can provide a cheap, simple, Doppler-scatter, passive-radar system. By tuning to a distant but powerful RF transmitter, aircraft and other objects will be observed from the reflections they send toward a receiver.
- A dual-coherent, passive radar can be created using two cheap SDR embedded systems running under the same clock-source “dual-coherent channel” receiver (also known as a multi-static receiver or coherent multichannel receiver). This will be used to create passive radar that can determine actual locations.[6]
- Multilateration (MLAT) and ADS-B could locate aircraft for as little as $40.[7]
- Small, smart, mobile sensors on UAV pods could jam locally at relatively low cost.
Toward Atlas-Scale Awareness
We must emulate Homer’s Atlas, all knowing and intelligent, through ubiquitous smart sensors powered by machine learning, AI, and big data and interacting to yield an intelligent maritime environment. If we can ingest mass quantities of raw RF signals using emerging big-data technologies in cheap sensors, we can use machine learning to create cognitive and intelligent radios for rapid decision making. Big data collected through smart SDR sensors will help produce ground truth for use in deep learning.
The intelligent-sea concept works well with the activity-based intelligence (ABI) in James Llinas and James Scrofani’s research at the Naval Postgraduate School, which describes it as “a discipline of intelligence where the analysis and subsequent collection are focused on the activity and transactions associated with an entity, a population or an area of interest.”[8] An SDR/ABI network can be used to:
- Collect, characterize, and locate activities and transactions
- Identify and locate actors and entities conducting these activities and transactions
- Identify and locate networks of actors
- Understand the relationships between networks
- Develop patterns of life
Llinas and Scrofani also describe ambient intelligence, a concept especially suited to SDR smart sensors. Ambient intelligence consists of “methods to design smart environments by fusing and exploiting sensor-laden environments toward inferring everyday life activities, tasks and rituals—these are ‘activity-based’ studies on human behaviors conducted from close-range sensing.”[9] Intelligence gathering of this type would have been prohibitively expensive a few years ago. Today, SDR smart sensors and cognitive/intelligent sensors promise an opportunity to realize ABI at sea.
Conclusion and Recommendation
Software development will ultimately drive the next generation of SDRs; the primary limitation today is programming, not hardware. Thus, we need not a Manhattan project but many small, targeted projects, like Operation Chastise, a rapidly developed, highly successful Royal Air Force initiative to build a totally new bomb to destroy dams in the Ruhr Valley during World War II. This was a small, though important, objective with little management and enabled by doers. By contrast, JTRS was a huge program that “was actually a bet against Moore’s Law—that it may be cheaper to make lots of single-purpose radios that plug together and get tossed when there’s an upgrade.”[10] We need projects that let the innovators innovate. Integration of SDRs is a multifaceted problem, but it’s manageable if confronted through many small initiatives.
The knowing sea can be ours. Innovation drivers key to SDR and other “third offset” (advantages primarily through technology) strategies include:
- Crowd sourcing and open sourcing of SDR software.
- Hackathons (DARPA has done this with SDR).
- Efforts like the Naval Postgraduate School’s “RoboDojo,” a dynamic opportunity for students, staff, faculty, and friends to tinker with robotic components and systems.
- STEM in the Navy and civilian communities.
- Programming literacy, standards, and RAD (rapid application development) in all operating systems and platforms.
- A focus on batteries, solar, and related power issues for our sensors.
- Cyber protection, including securing the supply chain of FLGA chips.[11]
U.S. adversaries will have no compunction in cutting through bureaucratic obstacles and restrictions to achieve knowledge dominance and national objectives. With every hurdle removed, will the Navy see $1,000 miniaturized Aegis systems and know everything about its opponent? Unlikely—but there is a window to maintain maritime dominance if the Navy acts now, and a dire risk of squandering it if it does not.
Endnotes
[1] C. Laufer, The Hobbyist’s Guide to the RTL-SDR: Really Cheap Software Defined Radio, (2018).
[2] Department of Defense. (2018). Summary of the 2018 National Defense Strategy of the United States of America. Washington: Department of Defense.
[3] L. W. Cunningham, Antiaccess / Area-Denial: Old Concepts, New Frontiers, A Monograph. Fort Leavenworth, Kansas: School of Advanced Military Studies (2015), United States Army Command and General Staff College, F.
[4] Cliff. (2014). Entering the Dragon’s Lair, China Military Development. Washiington, D.C.: Secretary of Defense.
[5] Vice Admiral Thomas Rowden, R. A. (2015, January). Distributed Lethality. Retrieved from Proceedings Magazine: http://www.usni.org/print/48262
[6] Laufer, 2018.
[7] Multilateration and ADS-B. (2018, February 19). Retrieved from Multilateration and ADS-B: http://www.multilateration.com/surveillance/multilateration.html
[8] J. L. Scrofani, FOUNDATIONAL TECHNOLOGIES FOR ACTIVITY-BASED INTELLIGENCE—A REVIEW OF THE LITERATURE. Monterey: Naval Postgraduate School, 2014.
[9] Scrofani, 2014.
[10] How to blow $6 billion on a tech project. (2012, June 18). Retrieved from ARS Technica: https://arstechnica.com/information-technology/2012/06/how-to-blow-6-billion-on-a-tech-project/2/
[11] Huffmire, T., Irvine, C., Nguyen, T. D., Levin, T., Kastner, R., & Sherwood, T. (2010). Handbook of FPGA Design Security. New York: Springer.
