Innovations in Machine Learning and their benefit to Electronic Warfare
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This webinar will explore two key problems for operatives analyzing the RF spectrum: signal classification and detection of anomalous signals.

 Export to Your Calendar 12/31/2018
When: Date TBA
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Where: United States

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Innovations in Machine Learning

and their benefit to Electronic Warfare

Date and Time to be Announced 

ABOUT

In recent years, there has been a huge acceleration in Machine Learning (ML) and Artificial Intelligence (AI) development. This has taken it from a concept little-appreciated outside of engineering and science circles, to something that we hear about on a daily basis. From driverless cars to intelligent home assistants, these technologies are anticipated to dramatically impact the way we live our lives. But how can we apply machine learning to the field of Electronic Warfare and what benefits will this bring?

This webinar will explore two key problems for operatives analyzing the RF spectrum: signal classification and detection of anomalous signals. Recent innovations mean that machine learning can be used to solve these problems more effectively and efficiently, freeing up operatives for more critical tasks.

Signal classification, including recognition of modulation type, is a complex problem which can be solved much more successfully with the use of trained neural nets. This allows signal types to be recognized with a very high degree of accuracy even in low Signal to Noise Ratio (SNR) conditions.

Detection of anomalous signals which indicate threat or interference is another key application which lends itself to a machine learning–based solution. Convolutional neural nets can be trained to distinguish ‘normal’ signals from anomalous signals possibly indicating a threat or unwanted interference. By training the net for a given application, it can be made to learn which signal types and anomalies are important and which are low priority.

Over the course of the webinar, Alistair Massarella will take you through the technical details of these applications and further explore the huge benefits machine learning is set to bring to the EW field.

PRESENTER

Alistair Massarella is Executive Chairman at spectrum monitoring company CRFS, which is leading the way in the development of machine learning technologies for RF spectrum applications. Alistair is both a practical and academic expert in all things wireless. He graduated with a PhD in telecommunications before spending three years in Antarctica with the British Antarctic Survey researching ionospheric physics using phased array radars.

He then went on to found several wireless companies including CBNL, before founding CRFS in 2007. CRFS’s mission is to provide high-performance systems for extracting intelligence from the RF environment. Developing machine learning is a key component in this mission.

 

REGISTRATION

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CONFIRMATION

After you register for the webinar, you will be emailed a confirmation with the link to the webinar, along with instructions on how to connect.

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You can also access the webinar via free mobile apps available on iOS, Android, and Blackberry devices.

For questions, please contact Brock Sheets.

VIEW RECORDED PRESENTATION

The entire presentation and Q&A will be recorded for viewing approximately 24 hours after the event. You can find the link to the recording in our AOC Virtual Series Archive. (separate registration required to view the recording)

With over 13,000 members internationally, the Association of Old Crows is an organization for individuals who have common interests in Electronic Warfare (EW), Electromagnetic Spectrum Management Operations, Cyber Electromagnetic Activities (CEMA), Information Operations (IO), and other information related capabilities. The Association of Old Crows provides a means of connecting members and organizations nationally and internationally across government, defense, industry, and academia to promote the exchange of ideas and information, and provides a platform to recognize advances and contributions in these fields.