Small unmanned aerial systems (sUASs) are quickly emerging as low-cost attritable aerial platforms for hostile reconnaissance, targeting, weapon delivery, and other purposes. Unlike legacy air targets, small unmanned aerial systems fly at low altitude, are easily masked by terrain features, often fly at relatively low velocities that render many forms of Doppler and time/frequency radar exploitation less effective, and produce small radar signatures due to their small sizes. Commercial market forces support an explosive future for sUASs platforms with increasing capability, decreasing cost, and increasing proliferation. There is a critical need to detect, classify, and track these platforms in cluttered urban environments with sufficient time to allow interdiction. To address this need, Toyon along with our university partner, proposes to leverage existing high-power millimeter-wave multiple input, multiple output (MIMO) transceiver technology. This technology will be paired with state-of-the-art tracking algorithms and artificial intelligence (AI) to provide a small and robust Ka-band radar capability for detection, classification, and tracking. At the conclusion of this proposed effort, the Toyon team will have developed preliminary hardware, software, and algorithmic designs leveraging existing work and will have demonstrated the feasibility of providing high-confidence tracks of small unmanned aerial systems using simulated and laboratory measurements.
