Data is king when it comes to developing new machine learning algorithms. So earlier this month SciTec conducted a field test to collect infrared and visible data on representative drone swarms to support development of new algorithms for the counter-small unmanned aerial system (sUAS) mission.
The threat of adversary or criminal use of sUAS against our military forces, critical infrastructure, and populace is a growing issue. To counter these threats, we first need to detect them, and preferably with automated systems. That’s where artificial intelligence and machine learning (AI/ML) comes in.
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“SciTec has an excellent reputation in developing AI/ML algorithms for missile warning and defense missions. We believe these same algorithms can be applied to the counter-UAS mission” said Damien Turchi, SciTec AI/ML Scientist and Principal Investigator for this field test. “We just needed to collect enough data on a variety of representative UAS threats with a number of different cameras to get started on training the algorithms. We executed this field test under our independent research and development (IR&D) program.”
The swarm scenarios featured up to nine sUAS ranging from a small custom racer to a commercial grade hexacopter. Pilots from the Drone Academy in Ringoes, New Jersey and SciTec, flew pre-planned patterns designed to represent swarming activity. Other SciTec operators supported data collection from a variety of commercial shortwave, midwave and longwave infrared (SWIR, MWIR, LWIR) and visible cameras. Preliminary data show great results with both point- and extended-source targets, multiple targets in a scene, and enough background clutter and confusers to provide real-world processing challenges.
“SciTec is engaging with a number of industry partners and Government agencies on the counter-UAS mission,” stated Craig Falci, Lead Portfolio Engineer. “This field test collection provides us unique data on heterogenous swarms to further develop our detection and tracking solutions.”
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Damien added, “Our frame processing software handled the imagery data right out of the box. We’ll continue our IR&D effort to tune and train our algorithms to provide a demonstrable threat warning capability we can deliver as part of a future integrated solution.”