ARCTIC: Advanced Radar Classification of Targets in Cluster
Award last edited on: 9/8/2022

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
Joshua Wilson

Company Information

nou Systems Inc (AKA: nSI)

7047 Old Madison Pike Suite 305
Huntsville, AL 35806
   (256) 327-5541
Location: Single
Congr. District: 05
County: Madison

Phase I

Contract Number: N/A
Start Date: 4/19/2022    Completed: 4/18/2024
Phase I year
Phase I Amount
Direct to Phase II

Phase II

Contract Number: HQ0860-22-C-7404
Start Date: 4/19/2022    Completed: 4/18/2024
Phase II year
Phase II Amount
Current ship-based radars encounter complex target scenes involving various types of clutter (e.g. debris, chuff, chaff, PID), challenging the ability of these radars to identify lethal targets and complete their critical mission. nou Systems, Inc (nSI) proposes a suite of physics-based algorithms integrated into a machine learning (ML) framework to mitigate the impact of the clutter and enable the radar to discriminate lethal objects in challenging clutter scenes. nSI has developed the primary components of this technology through multiple DoD contracts (including but not limited to a Phase I SBIR) and through internal research and development efforts. Because each of the individual physics-based algorithms and features are selected to be robust in the presence of clutter, the proposed solution inherently provides improved capability in these complex target scenes. nSI will prove the applicability of the developed technology to the SPY-1 and SPY-6 radars by training and testing the algorithms with government-provided radar data augmented with data developed through nSI's comprehensive radar modeling and scene generation toolset. By partnering with Lockheed Martin, Raytheon, and SEG, we will ensure the algorithms are suitable for insertion into the tactical radars. Because the ML framework intelligently fuses data from diverse algorithms involving various RF features, the proposed solution will improve capability when using existing waveforms and resource allocation, as well as when using planned or upgraded waveforms. Approved for Public Release | 22-MDA-11102 (22 Mar 22)