Virginia-based startup HawkEye 360 has come up with a unique
idea to bolster maritime domain awareness. The firm uses a constellation of
small satellites to detect and locate the source of commercial radio frequency
emissions – everything from VHF push-to-talk radios to maritime radar
transmissions, AIS beacons, satellite mobile comms and more.
HawkEye recently partnered with Amazon’s ML Solutions Lab to
incorporate machine learning algorithms into their analytics. Using AWS's
Amazon SageMaker Autopilot, they generated AI models to be used for an
automated maritime vessel risk assessment process.
Why is this revolutionary? Because it has the potential to
uncover hidden patterns and relationships among vessel features that previous
ML algorithms failed to do. Potential vessel behaviours of interest include
illegal fishing, human trafficking, ship-to-ship transfers, sanctions-busting,
GPS jamming and smuggling.
When bad actors turn off their AIS signal to hide their
vessel’s position, HawkEye can still spot the traces they leave by tracking
their radar and their VHF calls. “RF signals can provide valuable insight into
commercial vessel activity across the globe, even when some seek to hide their
location,” said HawkEye 360 vice president of products Tim Pavlick. “With these
machine learning-backed capabilities, we will empower customers to cut through
an ocean full of noise to obtain more timely and critical insights from
maritime RF data to improve mission outcomes and prevent illegal and illicit
activities.”
The ability to use ML algorithms to counter illicit drug
smuggling would be a first for the industry, even though the same approach has
been implemented on land. The reason is that there are so many variables and
complexities involved in the process for seagoing targets. With Amazon’s help,
HawkEye 360 says that it has overcome these challenges.
“By combining HawkEye’s data and deep domain expertise with
Amazon SageMaker Autopilot, HawkEye 360 is able to halve the time for machine
learning model development and deployment. That frees up time for data
scientists to focus on creating new and innovative solutions to the world’s
problems,” said Amazon's senior manager for machine learning solutions, Sri
Elaprolu.