TAK-ML: Applying Machine Learning at the Tactical Edge
The TAK-ML framework which supports data collection, model building, and model execution/employment in tactical environments, as well as a set of initial applications of this framework are described and evaluated, showing the capabilities available, the ease of use of the system, and initial insights into the efficacy of the resulting models and applications.
Abstract
The “Every Soldier is a Sensor” (ES2) concept employs warfighters' proximity to unfolding events in order to provide better situational awareness and decision-making capabilities. However, today's ES2 practices put the burden of data collection on warfighters themselves, and the burden of interpretation (across potentially many inputs) on commanders. This leads to a situation where data collection is limited by the capacity of the warfighter (who is busy executing their core objectives), and data fusion, interpretation, and analysis are limited by the cognitive constraints of the human commanders and analysts interpreting the potentially massive amounts of data. The TAK-ML framework transitions these burdens to machines, allowing collection, fusion, and learning to operate at machine speed and scale. To accomplish this, TAK-ML takes recent advancements in mobile device capabilities and machine learning techniques and applies them to the Tactical Assault Kit (TAK) ecosystem, e.g., ATAK mobile devices and TAK servers, to facilitate the easy application of ML to real mission sets. This paper describes the TAK-ML framework which supports data collection, model building, and model execution/employment in tactical environments, as well as a set of initial applications of this framework. The framework and applications are described and evaluated, showing the capabilities available, the ease of use of the system, and initial insights into the efficacy of the resulting models and applications.