Radio Frequency Machine Learning Q&A Session

Machine learning (ML) applied to radio frequency (RF) data has some unique challenges beyond applying ML to other fields. This is largely due to the nature of the RF domain, where there are very high data rates, dynamics, and a large degree of freedom space for parameters and context that can impact received signals. In naval, and defense applications in general, these are only more exacerbated by adversarial use, competition, disruption, and control of the electromagnetic spectrum and span across RF applications, including for electronic warfare, communications, radar, signal intelligence, etc. These challenges pose obstacles to transition RF ML research into real-world applications, especially within deep learning approaches which are data hungry.
In this talk we will discuss some recent successes in overcoming these challenges to enable emerging RF deep learning research to get closer to operational applications. In particular, we will cover the application of Transfer Learning and Data Augmentation to the RF domain. Transfer learning can use available and synthetic datasets to better learn deep learning features when operational datasets are not available. Similarly, data augmentation can create more robust deep learning networks and enable generalization into new operating conditions not yet experienced or only containing a few exemplars, such as with wartime reserve modes.
We will start with our recent work in RF transfer learning. First we will present results from large scale experiments with various types of pre-trained models as well as re-tuning, showing how to the lack of operational data during training can be overcome in many cases and can be mitigated to have graceful degradation in others. Next we will cover how to build an unsupervised model from synthetic data to learn lower level features that can transfer to a real-world classification task, demonstrating near identical results to the supervised training case of learning directly on the real-world operational dataset. Finally, we will discuss how to better learn features during training to handle the presence of new, unknown emitters that can be present during operation and guard against misclassifications and spoofing.
We will conclude with how data augmentation can enable successful transitions to real-world applications by surveying the successes of other domains, discuss initial work in RF data augmentation, and lay out a path forward. We will review work in image data augmentation to understand how to add variance to the environment and in the data capture process. We also review work from text augmentation to understand how to add variance to the source generation process. We then look at some initial RF data augmentation and layout what we believe the path forward is, highlighting areas where there are gaps that, when filled, can help get us closer to operational use of RF ML.


How to Make Radio Frequency Machine Learning Operational

**Moderator:** [Dr. Andrew Christianson](https://www.underline.io/speakers/79802-andrew-christianson)

**Speakers:**

[Ryan Crawford](https://www.underline.io/speakers/77577-ryan-crawford)

[Scott Kuzdeba](https://www.underline.io/speakers/77578-scott-kuzdeba)

[Eric Lorimer](https://www.underline.io/speakers/78914-eric-lorimer)

[Jennifer Galasso](https://www.underline.io/speakers/77555-jennifer-galasso)

[Jason (Alex) Marr](https://www.underline.io/speakers/77616-jason-(alex)-marr)


Radio Frequency Machine Learning

live discussion

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Scott Kuzdeba

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Radio Frequency Machine Learning Q&A Session

How to Make Radio Frequency Machine Learning Operational

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