core system module

Telemetry Normalization

Ensure movement data is comparable and reliable across environments, platforms, and collection methods. Telemetry Normalization standardizes raw inputs so downstream signals reflect behavior, not sensor artifacts.

KEY CAPABILITIES
  • Cross-source telemetry alignment
  • Timestamp normalization and correction
  • Coordinate system standardization
  • Noise reduction and smoothing
  • Consistent representation of movement states
system reliability

Why This Matters

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 signals risk reflecting sensor behavior rather than human execution. This leads to false positives, missed signals, and reduced trust in outputs.

Telemetry Normalization ensures that every signal Field IQ surfaces is based on comparable, interpretable data, regardless of how or where it was collected.

What Becomes Consistent

This module establishes a reliable foundation for all higher-level analysis.
Data Conditions Normalized
  • Variable sampling intervals
  • Coordinate drift and scale differences
  • Timestamp misalignment
  • Minor positional noise
  • Platform-specific telemetry quirks
Outcomes This Enables
  • Comparable signals across missions and teams
  • Reduced false deviation or dwell detection
  • More reliable readiness scoring
  • Higher confidence in instructor review

How Teams Experience This Module

During Operations or Training
Telemetry Normalization runs automatically in the background. Teams do not interact with it directly, but benefit from stable, trustworthy signals throughout the exercise.

During Review and Evaluation
Normalized data ensures that differences in performance reflect actual behavior rather than sensor inconsistencies, supporting fair and defensible evaluation.

Integration snapshot

Integration & Deployment

Designed to operate as a foundational layer inside Field IQ deployments.

Data Interface
Inputs
  • Raw movement telemetry from supported sources
  • Platform metadata and sampling characteristics
Outputs
  • Normalized telemetry streams
  • Consistent coordinate and timing representations
  • Clean inputs for downstream Field IQ modules
Execution & Control
Deployment Models
  • Embedded within Field IQ pipelines
  • Standalone Python module
  • Containerized service
  • Air-gapped compatible
Configuration
  • Normalization parameters defined in configuration files
  • Platform-specific adjustments supported
  • Core normalization logic remains fixed and deterministic
Security
Telemetry Normalization operates as a prerequisite layer for all Field IQ analysis modules.
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