Army posts sensor synthetic data SBIR opp
On October 12, the U.S. Army posted an applied small business innovation research (SBIR) announcement. Proposals are due by 12:00 p.m. Eastern on November 30.
The US Army requires large-scale, accurate and easily accessible training, test, and validation data to support AI model development for multiple security domains (e.g. SIPR, JWICS…). Sensor data is critical to develop AI/ML models. Unfortunately, there is not enough data yet to create highly performant models. Sensor Synthetic Data Generation will potentially serve to reduce bottleneck of training data supply that helps improve ML models by developing a synthetic data generation tool.
Currently, nearly all of the AI/ML models are developed using actual or representative data. There is not enough unique defense/intel data available to create performant models (e.g. it takes roughly 50M pieces of data to create a 60-70% performant model). Additionally, this data must be labeled; synthetically generated data has the ability to be labeled as it is generated, reducing human data labeling effort for real-world data and data generated from an external (e.g., vendor) source.
Sensor Synthetic Data Generation topic encompasses the development of a synthetic data generation tool for sensors (e.g. radar, etc.) that can augment the limited, labeled, training data available to support Artificial Intelligence / Machine Learning model development. The purpose of this topic is to lead to the creation/integration of mission-focused synthetic data to include but not be limited to: Priority Needs: Commercial Satellites/Electro Optical (EO) – World View 1,2,3 (Imagery), Digital Globe, Blacksky // Synthetic Aperture Radar (SAR) – RADARSAT and Capella; Other Needs: 0903 Full Motion Video (FMV) // Electronic Intelligence (ELINT) spectrums/waveforms // Variable Message Format (VMF) and Chat; Desired synthetic data to be used in AI/ML model development: Surface to Surface Radars, Surface to Air Missile Launchers, Tanks, Etc.
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