This interactive demonstration shows how Heimdall evaluates patterns using baseline analysis, persistence detection, and cross-sensor corroboration. Adjust the sliders to see how different signal characteristics affect detection confidence.
How consistent is the signal pattern? Higher values indicate more regular, repeating behaviors.
How long has the pattern been observed? Longer observation times increase confidence.
How many independent sensors agree? Multiple sensor types increase detection reliability.
Baseline Analysis: Heimdall establishes what "normal" looks like in your environment over time. Deviations from this baseline trigger initial attention. The "Regularity" slider simulates how different a pattern is from the established baseline.
Persistence Detection: Transient signals are weighted differently than sustained patterns. The system evaluates how long a deviation has persisted, with longer durations typically warranting higher confidence when combined with other factors.
Sensor Fusion: Multiple independent sensor modalities (acoustic, RF, visual, etc.) are cross-referenced. When independent sensors agree on a pattern, confidence increases substantially. This reduces false positives from single-sensor anomalies.
Transparent Output: Unlike black-box systems, Heimdall provides explainable confidence scores based on measurable signal characteristics. Operators understand why a detection occurred.
For security reasons, this demonstration does not include:
Qualified stakeholders may request a technical briefing for deployment evaluation.