TOYON RESEARCH CORPORATION — Department of Defense SBIR Phase I: AF221-0025

Description

On this effort, Toyon proposes leveraging, extending, and developing algorithms for autonomously detecting and tracking moving aerial targets observed in space-based EO/IR data collected by imaging sensors in low earth orbit (LEO). The proposed algorithm development will enable the derivation of moving target information (MTI) and multi-track analytics based on machine learning (ML) models. Vision-based geometry will be applied to mitigate parallax and potential occlusion/un-occlusion effects caused by satellite platform motion, while ML-based approaches will be leveraged and assessed for accentuating clutter suppression and performing target/clutter classification to derive MTI data. Track-before-detect and multi-track fusion applications developed at Toyon will be leveraged for extending tracks and assessing feasibility of fusing tracks for multi-sensor information to extend target track duration. The AFIT Sensor and Scene Emulation Tool (ASSET) will be utilized for simulating sufficient data sets for model training.