SBIR/STTR Award attributes
AiRANACULUS, along with its partners (Northeastern University, NWRA and L3Harris) proposes an innovative Waveform Agnostic learning-enhanced Decision Engine for any Radio (WADER) solution to meet the future needs of the army to counter sophisticated adversarial Electronic Warfare (EW) attacks and restore the Comms performance. The architecture consists of WADER EW Detection and Characterization (WEDC) Module which receives raw I/Q samples from RF Module. It also interfaces to the PHY, MAC and NET and receives radio statistics via the Management Information Base (MIBs). PHY / MAC / NET features consist of radio performance measurements such as RSSI, CINR, EVM etc. WEDC processes the RF samples to turn them into features (e. g. Cyclostationary Statistics). It combines these features with the radio performance measurements to detect and characterize the interference that is being encountered. These soft decisions are provided to the WADER Decision Engine (WDE) which uses machine learning over short term and long term along with game theoretic decision making to define the strategy and technique to mitigate the interference and restore the Comms performance.
