  Computer Vision (CV) systems are increasingly being adopted into Command and
Control (C2) systems to improve intelligence analysis on the battlefield, the
tactical edge. CV systems leverage Artificial Intelligence (AI) algorithms to
help visualize and interpret the environment, enhancing situational awareness.
However, the adaptability of CV systems at the tactical edge remains
challenging due to rapidly changing environments and objects which can confuse
the deployed models. A CV model leveraged in this environment can become
uncertain in its predictions, as the environment and the objects existing in
the environment begin to change. Additionally, mission objectives can rapidly
change leading to adjustments in technology, camera angles, and image
resolutions. All of which can negatively affect the performance of and
potentially introduce uncertainty into the system. When the training
environment and/or technology differs from the deployment environment, CV
models can perform unexpectedly. Unfortunately, most scenarios at the tactical
edge do not incorporate Uncertainty Quantification (UQ) into their deployed C2
and CV systems. This concept paper explores the idea of synchronizing robust
data operations and model fine-tuning driven by UQ all at the tactical edge.
Specifically, curating datasets and training child models based on the
residuals of predictions, using these child models to calculate prediction
intervals (PI), and then using these PI to calibrate the deployed models. By
incorporating UQ into the core operations surrounding C2 and CV systems at the
tactical edge, we can help drive purposeful adaptability on the battlefield.
