CRANE, Ind. --- Naval Surface Warfare Center, Crane Division (NSWC Crane) scientists and engineers have contributed Crane’s first open source project to provide warfighters with more advanced systems. The contribution to the open source software project was developed to support the Defense Advanced Research Projects Agency (DARPA) Radio Frequency Machine Learning Systems (RFMLS) program.
Paul Tilghman, the former RFMLS Program Manager in the Microsystems Technology Office at DARPA, says creating open source tools and standards are important to building advanced Radio Frequency (RF) systems.
“The DARPA RFMLS program is developing the foundations for applying neural networks to RF signal processing problems,” said Tilghman. “Creating a strong community that builds on each other’s work requires open source tools and standards. RF, unlike many other sensor modalities, doesn’t have existing datasets and because of its unique characteristics, requires a special standard for that data. The contributions of the NSWC Crane team to the SIG-MF standard are an important aspect of achieving this goal.”
Zachary Davis, an engineer at NSWC Crane, says applying machine learning (ML) to RF data is an emerging field of research and that this new standard lays the groundwork for the entire community.
“Our software contributions reflect Crane’s participation in this exciting field of research,” says Davis. “An open source software project is one that is freely available to the public and able to be modified and improved upon by the community. We have used the ‘master’ software for RFMLS purposes, modifying and expanding its capabilities in the process. By integrating our modifications back into the ‘master’ version of the software, we are allowing the RF machine learning community at large, comprised of academia, government, and industry, to leverage the work that we have done in order to keep pushing this area of research forward.”
Although Machine-learning technology is commonly used to identify faces in images, Andrew Christianson, a scientist at NSWC Crane, says ML technology can be applied to RF signals.
“One way to apply ML technology to RF signals, instead of identifying faces, is distinguishing communications systems such as differentiating an iPhone from a Samsung,”
Christianson says the updated standard is how computers will read RF signals.
“Computers can read and open image file types, such as JPEG or GIFs, because it is a standard way to encode an image,” says Christianson. “In 1989, an update to the standards allowed GIFS to animate. In a different example, the medical field updated the standard in JPEG quality to use in medical imaging. The JPEGs for medical imaging are much higher quality than what one might find searching the web, but they are built on the same standard. Someone took the old standard, modified it to be useful for their specific application, and then updated the old standard. Our contribution is a similar evolution of the new RF standard.”
Christianson says by changing the standard, this allows systems to be more efficient.
“When computers across the Department of Defense understand the standard, the systems are more efficient,” says Christianson. “Through this common standard, this also allows for transparency, collaboration, and agility are enabled. People can work together quickly to make improvements. The broad impact is that warfighters are equipped with more advanced tools and systems to do their work.”
NSWC Crane is a naval laboratory and a field activity of Naval Sea Systems Command (NAVSEA) with mission areas in Expeditionary Warfare, Strategic Missions and Electronic Warfare. The warfare center is responsible for multi-domain, multi- spectral, full life cycle support of technologies and systems enhancing capability to today's Warfighter.
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