Proteus – Machine Learning for RADAR

Starting in September 2023, the Deft Dynamics team is working full time on Tocaro Blue and the flagship product Proteus. ProteusCore is a C++ machine learning toolset embedded in a rugged marine hardware product, the ProteusHub hardware. This article describes some of the technology in ProteusCore. To learn more, visit Tocaro Blue.

Radar technology has existed for a long time. Over the years, innovations have taken place that have evolved Radar into a very capable sensor albeit with significant limitations. One such limitation is that its resolution is quite low, especially compared to optical and light-based sensors. For this reason, we feel that Radar has been overlooked as a sensor in the modern marine industry, especially recreational marine – Radar is a standard for collision avoidance, but its output is clunky and requires experience and training to interpret.

The ProteusCore is a machine learning pipeline that ingests Radar scan lines, and outputs a machine readable list of classified and tracked objects. The relative simplicity of this system marks a significant leap forward in Radar technology, and is a new basis for human interface focused displays. The ProteusCore can determine whether an object is a small boat, medium boat, or large boat, or a variety of buoys and markers, with confidence and complete automation.

ProteusCore Auto-focus eliminates the clutter that is found in traditional RADAR data using a machine learning (ML) filtering process that treats RADAR like a video stream and identifies real targets in the image. Typically we find that 85 to 90% of the contents of each RADAR scan are clutter that can be ignored – Proteus focuses the visualization and tracking on the remaining seaborn objects that really matter. The ML model sends feedback controls to RADAR to tune it in real-time to achieve optimal target detection.

After the Auto-focus has removed the clutter and detected objects, ProteusCore classifies the seaborn objects it detects to determine how they will behave. For instance, jetskis are quick and nimble – they can accelerate quickly, becoming a threat at any time. At the other end of the spectrum, barges have significant momentum and follow very predictable paths. Buoys can be displaced when tide currents are flowing. Class information is used to track targets more accurately and provide better predictive power.

Our high-confidence classification of targets and sensor fusion of your onboard sensors allows us to accurately simulate our own path and other vessel paths up to 30 seconds into the future. This simulation informs our at-a-glance Field of Awareness display, which keeps you informed of important closest-points-of-approach (CPA) with other vessels.

Did you know typical auto-pilot grade compasses are only accurate to within a few degrees? Even this accuracy can be thrown off by 5 degrees or more when near ships or bridges (that’s an error of 140 yards for a target at one mile range)! As ProteusCore™ combines data from your onboard navigation sensors with focused RADAR imaging, it is able to detect discrepancies between the sensors, model results, and cartography then amend these errors before providing results to the captain.

For recreational users, ProteusCore is embedded in ProteusHub.