This module standardizes source data so post-mission findings are based on comparable, interpretable inputs rather than collection artifacts. It helps ensure that movement analysis reflects observed execution, not inconsistencies in the underlying dataset.
Source data is rarely clean. Differences in devices, sampling rates, coordinate systems, and collection conditions can introduce inconsistencies that distort post-mission analysis if left unaddressed.Raw telemetry is rarely clean. Differences in devices, sampling rates, coordinate systems, and environmental conditions introduce inconsistencies that distort analysis if left unaddressed.
Without normalization, downstream findings can reflect collection artifacts rather than observed execution. That reduces confidence, increases false positives, and weakens trust in review outputs.Without normalization, downstream signals risk reflecting sensor behavior rather than human execution. This leads to false positives, missed signals, and reduced trust in outputs.
This module helps ensure that every Field IQ output is based on comparable, interpretable data, regardless of how or where it was collected.
During Data Preparation
This module runs as a background preprocessing step. Users do not interact with it directly, but they benefit from more consistent inputs across review workflows.
During Post-Mission Evaluation
Normalized data helps ensure that differences in findings reflect observed execution rather than inconsistencies in the underlying dataset, supporting fairer and more defensible review.